esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Verifying Crystallization Completion Time from Part Warpage  
To further verify the accuracy of the calculated crystallization time, additional experiments were performed with different cooling times (defined as the end of the packing time to the time at which the part was ejected) and correlated with warpage distribution along the cavity positions. The results are shown in Figure 10, with a graph illustrating the relationship between the warpage and cooling time shown in Figure 11. When the cooling exceeded 15 s, the warpage remained relatively constant, indicating the completion of crystallization. Conversely, for cooling times below 15 s, crystallization continued to occur, leading to subsequent shrinkage and the associated post-molding warpage. The measured crystallization completion time was approximately 24 s (noted with a red border in Table 6), which is consistent with that value (15 s cooling time plus 9 s packing time, Table 2).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model Temp.** & **Melt Temp.** & \multicolumn{4}{c}{**Crystallization Completion Time (S)**} \\ \cline{3-6}
**(\({}^{\circ}\)C)** & **(\({}^{\circ}\)C)** & **NG** & **MID** & **FG** & **Average** \\ \hline
40 & 200 & 21.01 & 20.39 & 19.16 & 20.18 \\
40 & 235 & 23.68 & 22.82 & 22.91 & 23.14 \\
40 & 270 & 25.70 & 24.81 & 25.26 & 25.26 \\
50 & 200 & 22.22 & 21.37 & 21.24 & 21.61 \\ \hline
50 & 235 & 24.35 & 24.15 & 23.69 & 24.06 \\ \hline
50 & 270 & 27.80 & 25.74 & 26.16 & 26.57 \\
60 & 200 & 24.77 & 22.70 & 23.89 & 23.78 \\
60 & 235 & 26.28 & 26.95 & 25.62 & 26.28 \\
60 & 270 & 30.50 & 28.44 & 28.09 & 29.01 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results of crystallization completion time at different cavity positions.  
Figure 10: Warpage distribution at different cavity positions with different cooling times.
# Efficient Resource Management for Sum Capacity Maximization in 5G NOMA Systems
## 3 Resource Management With Fixed Power Control
### User Channel Condition Based Sub-Channel Assignment Algorithm  
Here we design an efficient algorithm (represented as Algorithm 1) for sub-channel allocation to the users based on user channel condition under fixed transmit power at BS.  
```
1. Initialize \(K\), \(M\), \(H_{K\times M}\), \(P_{m}=P_{BS}/M\), \(\beta_{j,m}\), \(\mathcal{S}_{\text{max}}\) and \(R_{K\times 1}\).
2. For\(m=1:M\)do
3. Divide matrix \(H\) into two matrices \(H1\) and \(H1\).
4.\(H1=H(1:M,1:K/2)\).
5.\(H2=H(1:M,(K/2)+1:K)\).
6. For\(u=1:K/2\)do
7.\(B(H1(u),H2((K/2)+1-u))=1\).
8. For\(i=1:K\)do
9. For\(j=1:K\)do
10. If\(B(i,j)==1\)do
11. Compute the sum capacity using \(\beta_{j,m}\).
12. For\(P_{m}=5:1:P_{BS}\)do
13. Compute \(C_{i,m}=\log_{2}(1+\frac{\alpha P_{m}|h_{i,m}|^{2}}{n_{0}})\).
14. Compute \(C_{j,m}=\log_{2}(1+\frac{(1-\alpha)P_{m}|h_{j,m}|^{2}}{|h_{j,m}|^{2}p_{j,m}+n_{ 0}})\).
15. Compute \(C_{\text{sum}}=C_{i,m}+C_{j,m}\) on \(SC_{m}\).
16. End For\(P_{m}\).
17. End For\(j\).
18. End For\(i\).
19. End For\(u\).
20. At each index of \(m\), assign a sub-channel to any two users with high sum capacity.
21. End For\(m\).
22. Plot the sum capacity of the system.
```  
**Algorithm 1** Sub-channel Assignment Based on User Channel Condition  
In this algorithm, we first initialize \(K\) and \(M\), where \(M=K/2\). Then we allocate the transmit power for sub-channels as \(P_{m}=P_{BS}/M\). For each value of \(P_{m}\), the power of \(CU_{j}\) and \(CU_{i}\) must satisfy as \(p_{i,m}+p_{j,m}\leq P_{m}\), where \(p_{i,m}=\alpha P_{m}\) and \(p_{j,m}=(1-\alpha)P_{m}\), respectively. Note that \(\alpha\) is the fraction of power allocation which is lies between 0 and 1. Since we assume that the BS has perfect information of all its users, we generate a channel matrix \(H\) which is \(M\times K\) and sort its columns in decreasing order. In addition, we initialize the matrix \(R\) for user minimum capacity which is \(K\times 1\). Next, we divide matrix \(H\) into two matrices \(H1\) and \(H2\), where \(H1\) and \(H2\) are matrices of dimensions \(M\times K/2\). Now select a user from \(H1\) which has strongest channel gain and a user from \(H2\) which has weakest channel gain. If \(CU_{i}\in H1\) with strongest channel gain and \(CU_{j}\in H2\) with weakest channel gain, then allocate a sub-channel \(m\) which provide high sum capacity. This process will continue until all sub-channels are assigned to the users. At the end of said process, we compute the sum capacity of the system. The detail steps of this process are provided in Algorithm 1.
# Metabolic Engineering _Escherichia coli_ for the Production of Lycopene  
[PERSON]  
1 Energy-Rich Compounds Production by Photosynthetic Carbon Fixation Research Center, Shandong Key Lab of Applied Mycology, College of Life Sciences, Qingdao Agricultural University, Qingdao 266109, China; [EMAIL_ADDRESS]  
[PERSON]  
2 College of Food Science and Engineering, Qingdao Agricultural University, Qingdao 266109, China; [EMAIL_ADDRESS]  
[PERSON]  
1 Energy-Rich Compounds Production by Photosynthetic Carbon Fixation Research Center, Shandong Key Lab of Applied Mycology, College of Life Sciences, Qingdao Agricultural University, Qingdao 266109, China; [EMAIL_ADDRESS]  
[PERSON]  
1 Energy-Rich Compounds Production by Photosynthetic Carbon Fixation Research Center, Shandong Key Lab of Applied Mycology, College of Life Sciences, Qingdao Agricultural University, Qingdao 266109, China; [EMAIL_ADDRESS]  
Footnote 1: email: {[EMAIL_ADDRESS], [EMAIL_ADDRESS], [EMAIL_ADDRESS], jyming888@126.  
4-phosphate (MEP) pathway [16] or the mevalonate (MVA) pathway is reported [17]. The MEP pathway, present in many bacteria, algae, cyanobacteria, plant chloroplasts, and some eukaryotic parasites [18; 19], begins with the condensation of pyruvate and glyceraldehyde 3-phosphate derived from glycolysis [16; 20]. In contrast, the MVA pathway, present in most eukaryotes, fungi, plants, archaea, and some bacterial species, can produce IPP and DMAPP using acetyl-CoA as the initial substrate [19; 21].  
Engineered _Escherichia coli_ is widely used for the biosynthesis of secondary metabolites and high-value chemicals by metabolic engineering because of its rapid growth and powerful tools to enable genetic manipulation [22]. Moreover, the native MEP pathway present in _E. coli_ facilitates the production of terpen including lycopene. However, this pathway showed low metabolite flux in metabolic engineering [23], leading to the introduction of the heterogeneous MVA pathway into _E. coli_ for lycopene production.  
In this review, we summarized the recent advances in lycopene production by the engineered _E. coli_ using metabolic engineering strategies. Current investigations on the modification of the two metabolic pathways, MEP and MVA, for lycopene production in _E. coli_, were reviewed. The optimization of other metabolic pathways, modulation of regulatory networks, optimization of auxiliary carbon sources, and the fermentation process were also described. Furthermore, the common challenges, strategies, and prospects for lycopene biosynthesis in metabolically engineered _E. coli_ were discussed in this review.
## 3 Results and Discussion
### Sensory Scores
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Parameters** & **50-30** & **50-45** & **50-60** & **60-30** & **60-45** & **60-60** & **Control** \\ \hline  & \multicolumn{6}{c}{ATP-related compounds (\(\mathrm{\mu mol/g}\) sample)} \\ ATP & 0.25 \(\pm\) 0.04 & 0.21 \(\pm\) 0.03 & 0.19 \(\pm\) 0.06 & 0.25 \(\pm\) 0.03 & 0.24 \(\pm\) 0.05 & 0.22 \(\pm\) 0.04 & 0.25 \(\pm\) 0.03 \\ ADP & 0.87 \(\pm\) 0.05 \({}^{\mathrm{a}}\) & 0.86 \(\pm\) 0.03 \({}^{\mathrm{a}}\) & 0.64 \(\pm\) 0.03 \({}^{\mathrm{b}}\) & 0.85 \(\pm\) 0.02 \({}^{\mathrm{a}}\) & 0.55 \(\pm\) 0.04 \({}^{\mathrm{c}}\) & 0.56 \(\pm\) 0.03 \({}^{\mathrm{c}}\) & 0.48 \(\pm\) 0.05 \({}^{\mathrm{c}}\) \\ AMP & 5.30 \(\pm\) 0.12 \({}^{\mathrm{a}}\) & 5.43 \(\pm\) 0.09 \({}^{\mathrm{a}}\) & 4.29 \(\pm\) 0.10 \({}^{\mathrm{c}}\) & 5.02 \(\pm\) 0.12 \({}^{\mathrm{b}}\) & 4.18 \(\pm\) 0.27 \({}^{\mathrm{a}}\) & 4.26 \(\pm\) 0.22 \({}^{\mathrm{c}}\) & 4.07 \(\pm\) 0.06 \({}^{\mathrm{c}}\) \\ IMP & 12.59 \(\pm\) 0.85 \({}^{\mathrm{a}}\) & 13.09 \(\pm\) 0.69 \({}^{\mathrm{b}}\) & 13.17 \(\pm\) 0.21 \({}^{\mathrm{b}}\) & 13.14 \(\pm\) 0.35 \({}^{\mathrm{b}}\) & 14.31 \(\pm\) 0.47 \({}^{\mathrm{a}}\) & 14.25 \(\pm\) 0.70 \({}^{\mathrm{a}}\) & 13.30 \(\pm\) 0.38 \({}^{\mathrm{b}}\) \\ HxR & 0.88 \(\pm\) 0.03 \({}^{\mathrm{b}}\) & 0.91 \(\pm\) 0.04 \({}^{\mathrm{b}}\) & 0.91 \(\pm\) 0.05 \({}^{\mathrm{b}}\) & 0.90 \(\pm\) 0.06 \({}^{\mathrm{b}}\) & 0.94 \(\pm\) 0.07 \({}^{\mathrm{b}}\) & 0.92 \(\pm\) 0.04 \({}^{\mathrm{b}}\) & 2.89 \(\pm\) 0.06 \({}^{\mathrm{a}}\) \\ Hx & 0.27 \(\pm\) 0.05 & 0.28 \(\pm\) 0.04 & 0.25 \(\pm\) 0.02 & 0.25 \(\pm\) 0.03 & 0.27 \(\pm\) 0.03 & 0.25 \(\pm\) 0.03 & 0.26 \(\pm\) 0.03 \\ \hline  & \multicolumn{6}{c}{Sensory scores} \\ Odor liking score & 6.58 \(\pm\) 0.43 & 6.82 \(\pm\) 0.28 & 6.86 \(\pm\) 0.29 & 6.90 \(\pm\) 0.31 & 6.76 \(\pm\) 0.27 & 6.88 \(\pm\) 0.32 & 6.78 \(\pm\) 0.29 \\ Flavor liking score & 6.20 \(\pm\) 0.25 \({}^{\mathrm{d}}\) & 6.52 \(\pm\) 0.34 \({}^{\mathrm{d}}\) & 6.60 \(\pm\) 0.23 \({}^{\mathrm{d}}\) & 6.70 \(\pm\) 0.22 \({}^{\mathrm{c}}\) & 7.88 \(\pm\) 0.31 \({}^{\mathrm{a}}\) & 7.72 \(\pm\) 0.25 \({}^{\mathrm{a}}\) & 7.16 \(\pm\) 0.25 \({}^{\mathrm{b}}\) \\ Overall liking score & 6.36 \(\pm\) 0.31 \({}^{\mathrm{c}}\) & 6.74 \(\pm\) 0.28 \({}^{\mathrm{c}}\) & 6.60 \(\pm\) 0.30 \({}^{\mathrm{c}}\) & 7.04 \(\pm\) 0.22 \({}^{\mathrm{b}}\) & 7.46 \(\pm\) 0.21 \({}^{\mathrm{a}}\) & 7.62 \(\pm\) 0.37 \({}^{\mathrm{a}}\) & 6.56 \(\pm\) 0.24 \({}^{\mathrm{c}}\) \\ \hline  & \multicolumn{6}{c}{Mean \(\pm\) SD from triplicate determinations. Different lowercase superscripts in the same row indicate the significant difference (\(p<0.05\)).} \\ \end{tabular}
\end{table} 
Table 3: ATP-related compounds and sensory scores of SV-cooked tilapia processed by various SV conditions. focused on/mentioned in this study. In conclusion, the results clearly demonstrated that SV-cooked tilapia, particularly cooked at 60 \({}^{\circ}\)C for 45 and 60 min, provided a prime or desirable flavor better than traditional/directly cooked boiled fish, thus resulting in better overall acceptability of the product.
Although the combination of multi-view UAV imagery is a very successful approach in a variety of applications, nadir images combined with a dense distribution of GCPs constituted an ideal solution for the reconstruction of 3D agricultural surfaces [25]. On the other hand, oblique UAV imagery was considered particularly suitable for the three-dimensional modeling of buildings, cities, or urban settlements, presenting an enhanced achievable accuracy [26]. Moreover, oblique-viewing images can be utilized effectively for the 3D modelling of historical architectures and cultural heritage research, especially in areas characterized by limited accessibility [27]. In addition, oblique images increased by 50% the accuracy of 3D representations of topography in areas with high and steep slopes [28]. The current study aims to create a guideline for researchers who try to follow the UAV photogrammetric survey method for landslide mapping and monitoring, by exploring derived data quality (orthophotos and DSMs) in reference to data acquisition geometry. There are previous studies on the influence of the ground control number to the horizontal or vertical accuracy of orthophotos and DSMs, respectively [12; 13; 14; 15]. However, the current study is the first to conduct systematic research on UAV data acquisition methodology applied on multiple active landslides. UAV flights were performed over four landslide areas with different characteristics (land cover, slope, volume, dimensions, etc. ), and the collected data from each area were classified into three groups, depending on UAV acquisition geometry: (a) nadir-only images, (b) oblique-only images, and (c) an integration of nadir and oblique images. Moreover, a flat urban area was also surveyed for validation purposes. The processing of UAV imagery in each category was based on structure-from-motion (SfM) photogrammetry, leading to the creation of high resolution orthophotos and digital surface models (DSMs). Accuracy assessment was carried out using quantitative and qualitative comparative approaches, such as root mean square error calculation, length comparison,and mean center estimation. The derived results demonstrate the benefits of combining oblique and nadir images in order to reduce systematic errors and increase the overall accuracy of orthophotos and Digital Surface Models.
## 3 Electrode Station Configuration
### General Remarks
* The critical zone of the dam extends beyond it by 43.0 m (with a crest width of 5 m and a suspension distance of at least 1 m) along the xOx\({}^{\prime}\) axis (perpendicular to the dam), which is slightly better than those in the first (44.3 m) and third configurations (43.3 m) and slightly worse than that of the second (42.7 m). * The critical zone of the dam extends beyond it by 54.8 m (i.e., 27.4 m on either side) along the yOy\({}^{\prime}\) axis (parallel to the dam axis) in the coastal area. Despite the fact that the initially estimated required length of the assumed electrode station is 66.0 m (along the yOy\({}^{\prime}\) axis), the respective actual required length is 124.8 m (from the results of the superposition model in Table 3), while the available length is only 70 m.
* The developed absolute potentials and the corresponding values of the electrode station resistance, with respect to remote earth, are quite worse (26.14 kV, 22.40 \(\Omega\)) comparedto the first (22.77 kV and 19.5 \(\Omega\) according to Table 15 in [2]) and second configurations (22.48 kV and 19.26 \(\Omega\) of Table 1) and slightly better than that of the third (26.98 kV and 23.12 \(\Omega\) of Table 2). * In field behaviour results, there is a similarity in the case of single-frame non-operation, among the 1 st, 2 nd, 5 th, and 6 th frames, as well as between the 3 rd and 4 th, through an appropriate mirroring consideration. However, when determining the absolute potentials, there is a slight variation since the integration is always performed on the Ox semi-axis. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \multirow{2}{*}{**Supply Method**} & \multirow{2}{*}{\(\mathbf{J_{st}}\) **(m/m\({}^{2}\))**} & \multirow{2}{*}{\(\mathbf{d_{str\_Ox}}\) **(m)**} & \multirow{2}{*}{\(\mathbf{d_{str\_Ox}}\) **(m)**} & \multirow{2}{*}{\(\mathbf{\ell_{cud}}\) **(m)**} & \multirow{2}{*}{\(\mathbf{\ell_{cud}}\) **(m)**} & \multirow{2}{*}{\(\mathbf{\ell_{cbd}}\) **(m)**} & \multirow{2}{*}{\(\mathbf{E_{off}}\) **(m)**} & \multirow{2}{*}{\(\mathbf{E_{max}}\) **(V/m)**} & \multirow{2}{*}{\(\mathbf{V_{rel\_max}}\) **(V)**} & \multirow{2}{*}{\(\mathbf{R_{el}}\) **(m)**} \\  & & & & & & & & & \\ \hline \multirow{5}{*}{**Comp. **} & 6 frames operating & 18.33 & 47.02 & 47.02 & \(-\)59.28 & 59.28 & \(-\) & 22.09 & 23,378 & 20.03 \\ \cline{2-10}  & 5 frames operating & \multirow{2}{*}{22.00} & 49.01 & 47.40 & \(-\)62.41 & 54.10 & 3.09 & 26.13 & 26,142 & 22.40 \\  & (except for no. 6) & & & & & & & & \\ \cline{2-10}  & 5 frames operating & \multirow{2}{*}{22.00} & 46.40 & 48.06 & \(-\)61.67 & 57.25 & 2.57 & 26.14 & 25,914 & 22.20 \\  & (except for no. 5) & & & & & & & & \\ \cline{2-10}  & 5 frames operating & \multirow{2}{*}{22.00} & 47.60 & 44.72 & \(-\)60.64 & 59.11 & 1.82 & 26.08 & 23,915 & 20.49 \\  & 5 frames operating & \multirow{2}{*}{22.00} & 44.72 & 47.60 & \(-\)59.11 & 60.64 & 1.82 & 26.08 & 24,374 & 20.88 \\  & (except for no.
# Cyberattack on Flight Safety: Detection and Mitigation Using LoRa
### Jamming  
Jamming is the act of intentionally directing electromagnetic energy toward an RF-based receiver system to prevent or disrupt signal reception [11]. GNSS jammers broadcast their interference signal in the frequency band used for satellite navigation. A jamming attack can be categorized as a denial-of-service: the GNSS is still available, but its broadcast signals are exceeded by the jammer's power. Interference as a result of another communication can be a reason for the denial of the GNSS services in a geographical location, but jamming refers to the intentional interference with the GNSS services in some locations [13]. Jamming in ADS-B is similar to GPS jamming. In particular, the jamming (Figure 2a) is targeted at the ADS-B In packets that are already in the air. The attack is performed by transmitting sufficiently high-power radio signals at a 1090 MHz frequency. This radio noise disables ground station ATCs or aircraft equipped with ADS-B In from reading and processing ADS-B messages.
## 5 FEM Verification  
To verify the results of numerical calculations performed acc. \(M_{LTB,EL,2}\) programme and the results of analytical calculations made with approximation Formulas (14), (15) and (16), _LTBeam_ software (FEM) [40] was used. The software allows the adoption of the classic boundary conditions i.e., fork support or complete fixity. Also, it accounts for the beam elastic restraint against the section warping and against rotation in the plane of lateral torsional buckling. As mentioned already, the _LTBeam_ software version 1.0.11 contains an error in the units of the coefficient of the elastic restraint against warping (\(\alpha_{\omega}\)). The error results in the lowering of the actual value of \(\alpha_{\omega}\), when it is given in the commonly used unit [kNcm\({}^{3}\)/rad]. The drawback was eliminated in the _LTBeamN_ latest version, i.e., 1.0.3.  
For the sake of comparison, in checking computations, predetermined values of the indexes of fixity \(\kappa_{\omega}\) and \(\kappa_{u}\) were assumed. In this case, the stiffness of the elastic restraint against warping (\(\alpha_{\omega}\)) and the stiffness of the elastic restraint against lateral rotation (\(\alpha_{u}\)), which are necessary to make _LTBeam_ computations were determined from Formula (6 ab).  
Figure 3 shows the form of lateral torsional buckling of the beam (Figure 3b) for the critical moment determined with the _LTBeam_ programme.  
An exemplary IPE300 beam, with span \(L=5\) m, was loaded with a concentrated force applied at the midspan to the upper flange of the beam (\(z_{g}=+h/2\)). The elastic restraint against warping (\(\kappa_{\omega}=0.75\)) and lateral rotation (\(\kappa_{u}=0.5\)) of the beam at support nodes were taken into account. The critical moment of the lateral torsional buckling of beam was obtained, having the value of \(M_{cr}=163.71\) kNm (Figure 3a). The critical moments for beams analysed in this paper, for different types of sections, spansand load diagrams, and for different degrees of elastic restraint against warping and against lateral rotation, were determined in the way presented above. The results are discussed in Section 6.
# Joint Design of Transmit Waveforms for Object Tracking in Coexisting Multimodal Sensing Systems
## 4 Waveform-Dependent Performance Optimization Methods
### Approach I: Coexistence Waveform Design Approach by Minimizing System Interference
#### 4.2.1 Approach I-A: Radar Has Knowledge of Symbol Duration of Communications Users
This interference component can be written as:  
\[\tilde{\mathbf{X}}_{c}(\Psi_{c},T_{r},b_{r})=\mathbf{F}_{v}\ \mathbf{X}_{c}^{ \mathrm{H}}(\Psi_{c})\ \mathbb{S}_{r}(T_{r},b_{r})\, \tag{30}\]  
to emphasize its dependence on both the communications OLFM waveform parameters obtained by minimizing the MAI and the radar OFLM waveform parameters. Note that Equation (30) assumes that the actual communication users are transmitting continuously. The communications interference \(\tilde{\mathbf{X}}_{c}(\Psi_{c},T_{r},b_{r})\) can be minimized over all feasible radar OLFM waveform parameters that satisfy the following conditions:  
\[T_{\mathrm{min}}\leq T_{r}\leq T_{\mathrm{max}}\quad\mathrm{and}\quad\frac{B_ {\mathrm{min}}}{2T_{r}}\leq b_{r}\leq\frac{B_{\mathrm{max}}}{2T_{r}}\, \tag{31}\]  
Figure 4: (a) Plot of correlation function in Equation (29) as a function of the radar FM rate using \(b_{m}=b_{M}=B/(2T_{r})\) and (a) \(\tau=0\), (b) varying \(\tau\). Figure 5: (a) Transmit waveform design scheme for coexisting radar and communications systems. (b) Achievable radar OLFM waveform parameter set for minimizing the correlation in Equation (30). where \(\{T_{\rm min},T_{\rm max}\}\) and \(\{B_{\rm min},B_{\rm max}\}\) are the values of the minimum and maximum duration and bandwidth, respectively, of the radar transmit waveform. The minimum bandwidth, for example, can be obtained as \(B_{\rm min}\!=\!2b_{\rm r}T_{\rm r}\) for \(T_{\rm r}\!=\!T_{\rm min}\). The optimal radar ULFM waveform parameter set is thus obtained as:  
\[\hat{\Psi}_{\rm r}=\{\hat{T}_{\rm r},\hat{b}_{\rm r}\}=\arg\max_{(b_{\rm r},T_{ \rm r})\in\Psi_{\rm r}}\left\{\tr\left(\hat{\X}_{\rm c}^{\rm H}(\Psi_{\rm c},T_{ \rm r},b_{\rm r})\,\hat{\X}_{\rm c}(\Psi_{\rm c},T_{\rm r},b_{\rm r})\,\right) \right\}\,, \tag{32}\]  
where \(\tr(\cdot)\) denotes the trace of a matrix. Note that as the term minimized is the interference between radar and communications systems, the optimized radar parameter set \(\hat{\Psi}_{\rm r}\) affects the performance of both systems in reducing interference. An example of feasible radar parameters \(\hat{\Psi}_{\rm r}\) is demonstrated in Figure 5b using \(T_{\rm min}\!=\!1\ \mu\)s, \(T_{\rm max}\!=\!4\ \mu\)s, \(B_{\rm min}\!=\!3\ \mathrm{MHz}\), and \(B_{\rm max}\!=\!10\ \mathrm{MHz}\).
## 3 Results and Discussion
### Nitrosyl Pigments Content  
The highest amount of nitrosyl pigment content after production (48.5 ppm) was observed in the SL treatment (\(p<0.05\)). The amount of nitrosyl pigments in the C and M treatments after production was similar and reached 43.0 and 42.4 ppm respectively. The same tendency was observed after storage (Figure 2). [PERSON] et al. [40] determined the nitrosyl pigments level at 74.7 ppm in heat-treated pork ham cured with NaNO\({}_{2}\) at 150 mg/kg. However, [PERSON] et al. [9] reported 36.3 ppm of nitrosyl pigments in homogenized beef sausage cured with NaNO\({}_{2}\) at 200 mg/kg.  
A lower pH in the SL treatment was due to the metabolic activity of _L. fermentum_ S8 in raw meat batter during 24 h curing time and could have an influence on the reaction level of heme pigments with NO and thus leading to a higher amount of nitrosyl pigments in the SL treatment after cooking [3, 41]. _S. carnosus_ ATCC 51365 could take part in the curing process in raw meat batter reducing nitrate formed in the nitrite dismut reaction. In addition, nitrite could react again with meat heme pigments in raw meat batter. This process could take place many times during the 24 h of curing the meat and affect the amount of nitrosyl pigments in the SL treatment after cooking [15].  
_Lactobacillus_ bacteria are microorganisms that are relatively independent from iron. Independence from iron could most likely explain the capability of being able to grow in milk, in which the availability of this element is limited due to the presence of lactoferrin [42]. It is known that _Lactobacillus_ bacteria that are heme independent may reduce NO\({}_{2}\) and form NO and N\({}_{2}\)O [14]. _Limosiltabacillus fermentum_ used within this experiment was isolated from acidic whey and thus the capability to reduce NO\({}_{2}\) to NO in raw meat batter by bacteria cannot be excluded. NO may react with deoxymyoglobin (MbFe\({}^{2+}\)) and form nitrosylmyoglobin. The NO reaction with metmyoglobin (MbFe\({}^{3+}\)) is also possible and forms the nitrosyl metmyoglobin complex [4].  
Figure 2: Means (bars) and SD (line segments) of nitrosyl pigments in model meat products. a–b: different letters indicate significant differences among treatments (\(p<0.05\)). C—control treatment with NaNO\({}_{2}\) in an amount of 100 mg/kg; M—treatment with NaNO\({}_{2}\) in an amount of 50 mg/kg; SL—treatment with NaNO\({}_{2}\) in an amount of 50 mg/kg and _L. fermentum_ S8 at about 10\({}^{7}\) cfu/g and _S. carnosus_ ATCC 51,365 at about 10\({}^{7}\) cfu/g.