esa-sceva/satcom-chunk-collection huggingface dataset:
# Secure Transmission for Buffer-Aided Relay Networks in the Internet of Things
## 3 Transmission Schemes
### SCJ-OPA Scheme  
To further enhance the physical layer security for the SCJ scheme, the optimal power allocation operation is employed at the source sensor node under the SCJ-OPA scheme. Similar to the section above, we still assume that \(P_{R_{3}}=P_{Total}/2\). Then, we have \(P_{S_{3}}+P_{J_{3}}=P_{Total}/2\). Given \(P_{S_{3}}=\alpha P_{Total}/2\) and \(P_{J_{3}}=\left(1-\alpha\right)P_{Total}/2\) where \(0<\alpha<1\) denotes the power allocation factor. We aim to find the optimal power allocation factor to minimize the secrecy outage probability of the considered system. Therefore, the optimization problem can be written as  
\[\min_{\begin{subarray}{c}\alpha\\ \text{s.t. }0<\alpha<1\end{subarray}}P_{out}, \tag{17}\]  
where \(P_{out}\) represents the overall secrecy outage probability of the system. We derive the expression of the secrecy outage probability and solve the optimization in the following section. Moreover, making an appropriate substitution of the parameters, i.e., \(P_{S_{2}}\to P_{S_{3}}\) and \(P_{J_{2}}\to P_{J_{3}}\), the received SNR or SINR at the corresponding node under the SCJ-OPA scheme can be obtained easily, hence is omitted.  
Now, according to the authors of [36; 37], the achievable secrecy rate of the first and second hops can be, respectively, expressed as  
\[C_{SRE}^{\star}=\left[\log_{2}\left(1+\gamma_{SR_{i}}^{\star}\right)-\log_{2} \left(1+\gamma_{1,E}^{\star}\right)\right]^{+}, \tag{18}\]  
\[C_{RDE}^{\star}=\left[\log_{2}\left(1+\gamma_{R_{j}D}^{\star}\right)-\log_{2} \left(1+\gamma_{2,E}^{\star}\right)\right]^{+}, \tag{19}\]  
where \(\star\in\left\{NJ,SCJ,SCJ-OPA\right\}\), \(\gamma_{1,E}^{\star}\in\left\{\gamma_{1,NCE}^{\star},\gamma_{1,CE}^{\star}\right\}\), \(\gamma_{2,E}^{\star}\in\left\{\gamma_{2,NCE}^{\star},\gamma_{2,CE}^{\star} \right\}\), \(\left[x\right]^{+}=\max\left\{0,x\right\}\).
# Characterization of Nanoparticle Batch-To-Batch Variability
## 4 Discussion
### Size Distributions  
* Depending on the synthesis conditions, the size distributions of NM batches can be reasonably well controlled with low variability and high reproducibility.
* The particle size distributions of NMs from liquid phase synthesis are narrow, however partial agglomeration is observed, especially for ZnO, TiO\({}_{2}\) and CeO\({}_{2}\) NMs, both in solution and in aerosols.
* Silica materials from flame synthesis are mostly aggregated and have broader size distributions.
* The particle sizes determined by DLS are usually larger and more variable even for NMs produced by the same synthesis route than the diameters determined by SMPS. Moreover, some particles become de-agglomerated by spraying them into the gas phase and drying afterwards, on the other hand further agglomeration could occur in solution after the synthesis.
* Post-synthesis treatments commonly applied in the purification of NM dispersions, such as centrifugation approaches, can cause shifts in dispersion size distributions due to particle aggregation [49].
* For liquid phase synthesis, dispersion reproducibility can be \"designed into\" the synthetic procedure by following optimization processes which involve careful parameter variation as precursor concentrations or temperature (see Supplementary Materials S1) coupled with extensive characterization.
## 3 Measuring Equipment and Measured Results  
The measured results were collected from the vessel's departure from the port. Measurement commenced at dead slow ahead of the main engine and as the engine load increased until 83 rpm. Data were collected for the 22 different running points of the low-duty turbo compressor. The measurements included gas inlet and outlet temperatures, gas inlet and outlet pressures, and the mass flow rate of the main boiler. Two mirror-oriented main boilers that consume delivered gas were placed on the vessel. The measured results are listed in Table 2 and measurement equipment was described in previous studies [42; 43; 44; 45]. The LNG cargo consists of the following components by mass ratio [46]: CH\({}_{4}\) (0.87955), C\({}_{2}\)H\({}_{6}\) (0.08328), C\({}_{3}\)H\({}_{8}\) (0.02056), C\({}_{4}\)H10\({}_{1}\) (0.00272), C\({}_{4}\)H10\({}_{n}\) (0.00400), and N\({}_{2}\) (0.00989).  
The measured values for the average, frequency, and the power factor were collected at the high voltage control breaker, which monitors current, frequency, and power factor value at the inlet of the electric motor (Table 3). The measured power factor was the same within the entire measured range, \(\varphi=0.9\). The electric motor speed was measured from the HIMAP M, which is a multifunctional digital protection relay with measuring functions, [47]. The maximal electric motor speed was 3588 rpm  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter.** & **Unit** & **Natural Gas Type 1** & **Natural Gas Type 2** \\ \hline Molecular weight & kg/kmol & 18.64 & 18.44 \\ Flow & Nm\({}^{3}\)/h & 8500 & 4000 \\ Inlet pressure & kPa & 103 & 103 \\ Inlet temperature & \({}^{\circ}\)C & \(-\)40 & \(-\)140 \\ Outlet pressure & kPa & 200 & 200 \\ Shaft speed & rpm & 24,000 & 17,000 \\ Isentropic efficiency & \% & 77\% & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Turbo compressor design data [41].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Main Propulsion** & **Gas Temperature** & **Gas Pressure in** & **Gas Temperature** & **Gas Pressure Out** & **Mass Flow (kg/h)** \\
**Shaft Speed (rpm)** & (°C) & **(MPa)** & **Out (°C)** & **(MPa)** & **Mass Flow (kg/h)** \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 2: Measured values of natural gas before and after low-duty turbo compressor.  
and the turbo compressor running range is 12,000 to 24,000 rpm, as per the manufacturer. From that information, we calculated the running speed of the turbo compressor using the measured electric motor speed and following relation:  
\[C_{rpmn}=\frac{EM_{rpmn}\cdot 24000}{3585}. \tag{1}\]
# Nonlinear Stiffness of Semi-Fixed Dowel Joints in Semi-Integral Bridges
## 6 Summary and Conclusions  
Taking the real joint in a retrofitted jointless bridge as a prototype, full-scale semi-fixed dowel joints were tested, their nonlinear stiffness were analyzed, and the following conclusions can be drawn in this paper:  
(1) The horizontal and rotation displacement of the semi-fixed dowel joint increase with the increase of loading displacement. At the beginning of loading, the deformation of the joint was mainly caused by compression of the rubber sleeve, and then by rotation of the joint.  
(2) The steel dowels in the semi-fixed dowel joints behaved from the elastic stage to the elastic-plastic stage in the whole loading process. The strains measured from the bottom concrete block were all in the elastic ranges, and the values decreased as strain gauges are located from shallow to deeper.  
(3) The relationships between the radial stiffness or flexural stiffness of the semi-fixed dowel joint and the loading displacement are nonlinear. The stiffness decreases with the increase of the loading displacement, and is affected greatly by the wall thickness of the rubber sleeve around the steel dowels. However, the influence of the thickness of the rubber pad is small and can be ignored in practical engineering.  
(4) If the deformation of 10 mm in the longitudinal beam caused by the maximum temperature effect is taken as the design allowable value, the wall thickness of the rubber sleeve should not be less than 40 mm in order to ensure that the dowel joint behaves in the linear elastic range for the case studied.  
(5) According to the test results and mechanical analysis, the calculation formula for radial and flexural stiffness of semi-fixed dowels were proposed, which is an interesting topic for further research by more specimen tests and numerical analysis.  
All authors substantially contributed to this work. [PERSON] designed and fabricated the semi-fixed dowel joint model. [PERSON] and [PERSON] designed the experiment. [PERSON] and [PERSON] performed the experiments and analysis. [PERSON] and [PERSON] wrote the paper. [PERSON], [PERSON] and [PERSON] revised and finalized the paper. All authors helped with the writing of the paper. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China with Grant Numbers 51578161.
# Sustainable Cold Chain Management: An Evaluation of Predictive Waste Management Models
## 3 Methodology
### Modeling: ARIMA and MLR
#### 3.3.3 Data Analysis and Implementation  
The modeling process followed the structured phases of the CRISP-DM framework:  
1. Business Understanding: Defined Company A's primary objectives for waste reduction and operational efficiency. Historical sales data and its limitations (e.g., lack of IoT metrics) were identified during this phase.
2. Data Exploration and Preparation: Sales records were analyzed for inconsistencies such as skewed distributions and missing values. Cleaning steps included addressing negative values due to promotional adjustments and rebalancing skewed customer distributions.
3. Model Development: The ARIMA model was calibrated to capture temporal demand trends, while MLR integrated multivariable drivers of demand. Seasonal decomposition and residual analysis were performed to refine forecasts.
4. Evaluation: The models were evaluated using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), ensuring predictive accuracy and robustness.  
By explicitly defining the variables used in both ARIMA and MLR models, the analysis provided a dual-layered approach to align inventory levels with real-world demand fluctuations, mitigate waste risks, and enhance operational efficiency.
# Initialize GroupKFold for cross-validation group_kfold = GroupKFold(n_splits = 5)
## 7 Conclusions  
This study has revealed several key insights into the dynamics of information diffusion with time delay mechanisms. Most notably, we discovered that while network structure and spread probability significantly impacted information coverage, the specific delay mechanism type (constant, uniform, exponential) showed no statistically significant effect. Furthermore, our analysis identified temporal dynamics--specifically, the timing of peak diffusion speed--as the dominant predictor of ultimate information reach.  
Our findings demonstrated that temporal dynamics, particularly peak diffusion speed timing, play a crucial role in determining ultimate information coverage. The Gradient Boosting model achieved strong predictive performance (R\({}^{2}\) = 0.847 \(\pm\) 0.032) on synthetic data, identifying peak_speed_time as the dominant predictor (importance = 0.848).  
Transmission probability (p_spread) emerged as consistently significant across all analyses, with optimal values of 0.3-0.4 maximizing coverage. Interestingly, the specific delay mechanism (constant, uniform, exponential) showed no statistically significant effect on coverage, challenging assumptions in previous research.  
Network structure also influenced diffusion outcomes, with Random networks achieving the highest average coverage (0.653) and Tree networks showing lowest coverage (0.219). The strong verification correlation between peak_speed_time and coverage (r = 0.995, \(p<\) 0.001) confirmed the fundamental importance of temporal dynamics in information spread.  
This research contributed to the field by systematically comparing delay mechanisms across diverse network structures, quantifying the relative importance of temporal versus structural factors, and providing practical recommendations for optimizing information dissemination. The integration of real data analysis with synthetic modeling offered methodological insights for future network research.  
Conceptualization, O.K.; methodology, O.K.; data curation, K.B. and O.O.; formal analysis, K.B. and R.S.; investigation, K.B., R.S., O.O. and I.A.; writing--original draft preparation, K.B. and A.S.; writing--review and editing, O.K., K.B. and A.S.; supervision, A.S.; funding acquisition, I.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Publicly available datasets were analyzed in this study. This data can be found here: [[https://colab.research.google.com/drive/1](https://colab.research.google.com/drive/1) zAxPi7i6 EKtuNqJ5 dsodpl2J3_Z787m]([https://colab.research.google.com/drive/1](https://colab.research.google.com/drive/1) zAxPi7i6 EKtuNqJ5 dsodpl2J3_Z787m).  
The authors declare no conflicts of interest.
## 4 Robust Design Based on Cutting-Set Method
### Total Algorithm of Robust Resource Allocation and Trajectory Planning  
Based on the cutting-set method, we propose a robust resource allocation and trajectory planning algorithm to solve the uncertainty of users' location in this subsection. The detailed robust algorithm is shown in Algorithm 2.  
As shown in Algorithm 2, the finite subset of potential users' locations are firstly initialized. Firstly, for a given finite subset of users' locations, the optimal computation offloading variables \(P_{o,n}^{k}[t]\), \(f_{n}^{k}[t]\), \(f_{n}^{k}[t]\) and UAV trajectory \(\textbf{q}_{n}^{k}[t]\) are obtained by solving **P5** with Algorithm 1. Then, based on the UAV trajectory from the previous step, we can obtain the worst-case users' location and add it into \(\mathcal{S}_{n}^{k}\) for the next turn of optimization. The robust algorithm processes alternately until the improvement reaches the stable point or reaches the maximum number \(k\) of iterations.  
```
Initialization: Set the iterative number \(k=1\). Input: Initialize finite subset \(\mathcal{S}_{n}^{k}\). repeat  calculate \(P_{o,n}^{k}[t]\), \(f_{n}^{k}[t]\), \(f_{u}^{k}[t]\) and \(\mathbf{q}_{u}^{k}[t]\) by Algorithm 1;  compute \(\mathbf{q}_{n}^{w,k}\) for given \(\mathbf{q}_{u}^{k}[t]\);  update finite subset \(\mathcal{S}_{n}^{k+1}=\{\mathcal{S}_{n}^{k},\mathbf{q}_{n}^{w,k}\}\);  update \(k=k+1\); until reach the stable point or the maximum iterative number \(k\) Output\(P_{o,n}^{k}[t]\), \(f_{n}^{k}[t]\), \(\tilde{f}_{u}^{k}[t]\), \(\mathbf{q}_{u}^{k}[t]\).
```  
**Algorithm 2** Robust Offloading Trajectory and Computation Offloading Algorithm with uncertainty of users' location
# A Heat Load Prediction Method for District Heating Systems Based on the AE-GWO-GRU Model
## 2 Methodology
### GRU Prediction Model Based on GWO
#### 2.2.1 Gated Recurrent Unit  
The GRU neural network is an improved version of the LSTM neural network. Compared to the standard LSTM network, the GRU reduces the gating mechanisms from three to two: the reset gate and the update gate. This simplification enhances the ability to capture the correlation between time and information [30]. GRU facilitates input updates for long data sequences and mitigates the vanishing gradient problem, improving iteration efficiency and prediction speed without sacrificing accuracy. The GRU network structure is shown in Figure 3.  
Figure 2: Overall system framework diagram.  
In the figure, \(r_{t}\) represents the reset gate, \(z_{t}\) represents the update gate, \(\widetilde{h}_{t}\) represents the candidate hidden layer, \(h_{t}\) represents the hidden layer, and \(\sigma\) represents the Sigmoid function. The reset gate \(r_{t}\) controls the amount of candidate hidden layer information from time \(t\)-1 that is passed into time \(t\). The update gate \(z_{t}\) controls the amount of state information updated from time \(t\)-1 to time \(t\). The candidate hidden layer \(\widetilde{h}_{t}\) calculates the candidate hidden state at time \(t\), and then combined with the reset gate to determine the final hidden state. The hidden layer \(h_{t}\) stores information from time \(t\)-1 and passes it to time \(t\). The GRU network update formulas are as follows:  
\[r_{t}=\sigma(W_{r}\cdot[h_{t-1},x_{t}]) \tag{1}\]  
\[z_{t}=\sigma(W_{z}\cdot[h_{t-1},x_{t}]) \tag{2}\]  
\[\widetilde{h}_{t}=\tanh\bigl{(}W_{\widetilde{h}}\cdot[r_{t}\ast h_{t-1},x_{t}] \bigr{)} \tag{3}\]  
\[h_{t}=(1-z_{t})\ast h_{t-1}+z_{t}\ast\widetilde{h}_{t} \tag{4}\]  
where \(W_{r}\), \(W_{z}\) and \(W_{\widetilde{h}}\) represent the weight matrix of reset gate, update gate and candidate hidden layer, respectively. \"[]\" denotes the connection of two matrices, \"*\" denotes the matrix product, and \".\" denotes the point multiplication.
## 5 Experiments and Results
### Performance Evaluation
#### 5.2.2 Hardware Implementation Overhead
\begin{array}{l}\text{The size of HI${}_{\text{REF}}$: SIZE${}_{\text{REF}}$}=\frac{64\times\text{Total${}_{\text{BB}}$}}{8\times 1 024}\\ \text{The size of FE${}_{\text{Address}}$ Bitmap}=\frac{\text{SIZE${}_{\text{program}}$}}{64}\\ \text{The size of BB${}_{\text{Address}}$ Bitmap}=\frac{\text{SIZE${}_{\text{program}}$}}{64} \\ \text{Total}=\frac{64\times\text{Total${}_{\text{BB}}$}}{8\times 1024}+\frac{ \text{SIZE${}_{\text{Program}}$}}{64}+\frac{\text{SIZE${}_{\text{Program}}$}}{64} \end{array}  \tag{4}\] \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Benchmarks** & **Size of CFI\({}_{\text{label}}\) (bits)** & **Size of HI\({}_{\text{REF}}\) (KB)** & **FE\({}_{\text{Address}}\) Bitmap (KB)** & **BB\({}_{\text{Address}}\) Bitmap (KB)** & **Total (KB)** \\ \hline OpenECC & 16 & 52.61 & 3.08 & 3.08 & 58.77 \\ SHA1 & 13 & 26.56 & 1.34 & 1.34 & 29.24 \\ FFT & 12 & 16.74 & 1.42 & 1.42 & 19.58 \\ CRC16 & 12 & 25.24 & 1.23 & 1.23 & 27.7 \\ BasicMath & 14 & 33.80 & 2.33 & 2.33 & 38.46 \\ AES & 15 & 27.62 & 1.44 & 1.44 & 30.5 \\ Bitcount & 12 & 26.12 & 1.30 & 1.30 & 28.72 \\ Blowfish & 12 & 25.36 & 1.30 & 1.30 & 27.96 \\ Patricia & 12 & 30.10 & 1.52 & 1.52 & 33.14 \\ QuickSort & 11 & 7.96 & 1.23 & 1.23 & 10.42 \\ \hline Average & - & 27.21 & 1.62 & 1.62 & 30.45 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The size of the reference information and the bitmap for the selected benchmarks. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Slice Logic** & **SoC** & **CPU Core** & **HMM** \\
**Utilization** & & & \\ \hline Slice Registers & 6584 & 2604 & 612 \\ Slice LUTs & 14,259 & 7208 & 1374 \\ Occupied Slices & 5213 & 3312 & 486 \\ BlockRAM/FIFO & 52 & 13 & 48 \\ BUFG/BUFFGCTRLs & 7 & 1 & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The FPGA resources used for the security-enhanced embedded system.
# Structural Modal Time Domain Identification Method Based on the Bayesian Uncertain Quantification
## 4 Numerical Examples
### Three Degrees of Freedom Spring-Mass System
#### 4.1.1 Nominal Case  
The noise level was assumed to be 15%, and the sampling point for each measured channel was 2500. Figure 3 shows the acceleration excitation and displacement response time domain signals of the system. For the optimization solution, the initial values of the natural frequencies were given by the resonance peak of the power spectral density (PSD) of the measured displacement response, and the initial values of the damping ratios were all 0.5%.  
The results of the modal parameter identification using the proposed method are presented in Table 1. For the natural frequencies, damping ratios, and modal contribution factors, the identification results of the first mode were in perfect agreement with the theoretical values, and the identification results of the remaining two modes had a maximum error of only 4% compared to the theoretical values. The MAC between the identified and theoretical mode shapes was close to 1, indicating that the identified mode shapes were accurate. In addition, the MPV of the variance of the identified prediction error \(\sigma_{e}^{2}\) was \(8.49\times 10^{-7}\) and its theoretical value was \(8.75\times 10^{-7}\), with a relative error of only 2.97%.  
Figure 3: Acceleration excitation and measured displacement response of the spring–mass system. (**a**) Sinusoidal swept acceleration excitation; (**b**) Displacement response of mass 1; (**c**) Displacement response of mass 2; (**d**) Displacement response of mass 3.  
Figure 2: Three degrees of freedom spring–mass system.  
The uncertainties in the identified parameters are listed in Table 2. The posterior uncertainties of the natural frequencies and damping ratios were quantified using the coefficient of variation (COV, COV = posterior standard deviation/MPV). For the natural frequencies, the posterior COVs were very small (\(<\)0.03%), whereas the COVs of the damping ratios were above 0.4%, up to 2.869%, indicating that the posterior uncertainties of the natural frequencies were smaller than the damping ratios; this was consistent with typical observations [14; 19]. In addition, the COV of the variance of the prediction error \(\sigma_{\epsilon}^{2}\) was 1.633%.
## 3 Results
#### 3.1.3 Main Volatile Compounds  
The results of the influence of sequential fermentation using _T. delbrueckii_/_S. cerevisiae_ DiSVA 709 in comparison with pure _S. cerevisiae_ DiSVA 709 and commercial strain OKAY\({}^{\text{\textregistered}}\) fermentations on the volatile profiles of the wines are shown in Table 2.  
The addition of SO\({}_{2}\) did not show any relevant influence on the production of volatile compounds, except for ethyl acetate. Indeed, in both pure fermentations of _S. cerevisiae_ (OKAY\({}^{\text{\textregistered}}\) and DiSVA 709), the absence of SO\({}_{2}\) induced a production of ethyl acetate double or more if compared with the trial with SO\({}_{2}\) added, while the presence of _T. delbrueckii_ \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Inoculated Strains** & **Ethanol** & **Total Acidity** & **Volatile Acidity** & **Malic Acid** \\  & **(\%\%/\(\epsilon\))** & **(g/L)** & **(g/L)** & **(g/L)** \\ \hline _S. cerevisiae_ OKAY\({}^{\text{\textregistered}}\) + SO\({}_{2}\) & 14.69 \(\pm\) 0.01 \({}^{\text{a}}\) & 5.68 \(\pm\) 0.02 \({}^{\text{c}}\) & 0.30 \(\pm\) 0.00 \({}^{\text{a}}\) & 0.9 \(\pm\) 0.00 \({}^{\text{b}}\) \\ _S. cerevisiae_ OKAY\({}^{\text{\textregistered}}\) & 14.88 \(\pm\) 0.02 \({}^{\text{a}}\) & 5.27 \(\pm\) 0.01 \({}^{\text{c}}\) & 0.31 \(\pm\) 0.01 \({}^{\text{a}}\) & 0.7 \(\pm\) 0.00 \({}^{\text{b}}\) \\ _S. cerevisiae_ DiSVA 709 + SO\({}_{2}\) & 14.05 \(\pm\) 0.12 \({}^{\text{b}}\) & 6.29 \(\pm\) 0.05 \({}^{\text{a}}\) & 0.22 \(\pm\) 0.01 \({}^{\text{a}}\) & 1.25 \(\pm\) 0.07 \({}^{\text{a}}\) \\ _S. cerevisiae_ DiSVA 709 & 14.35 \(\pm\) 0.1 \({}^{\text{b}}\) & 5.77 \(\pm\) 0.00 \({}^{\text{ab}}\) & 0.30 \(\pm\) 0.01 \({}^{\text{a}}\) & 1.00 \(\pm\) 0.00 \({}^{\text{ab}}\) \\ _[PERSON]_ DiSVA 130/_S. cerevisiae_ DiSVA 709 & 13.9 \(\pm\) 0.02 \({}^{\text{c}}\) & 6.14 \(\pm\) 0.14 \({}^{\text{a}}\) & 0.25 \(\pm\) 0.00 \({}^{\text{a}}\) & 1.35 \(\pm\) 0.07 \({}^{\text{a}}\) \\ _T. delbrueckii_ DiSVA 130/_S. cerevisiae_ DiSVA 709 & 13.86 \(\pm\) 0.09 \({}^{\text{c}}\) & 5.35 \(\pm\) 0.03 \({}^{\text{c}}\) & 0.29 \(\pm\) 0.00 \({}^{\text{a}}\) & 1.45 \(\pm\) 0.07 \({}^{\text{a}}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Main oenological characters of _T. delbrueckii_ in sequential fermentation with and without SO\({}_{2}\) added as compared to fermentation with pure _S. cerevisiae_ starter strains. Data are the means \(\pm\) standard deviation. Data with different superscript letters (a,b,c) within each column are significantly different (Duncan tests; \(p<0.05\)).