esa-sceva/satcom-chunk-collection huggingface dataset:
# Probiotics in the Sourdough Bread Fermentation: Current Status
## 1 Introduction  
Sourdough fermentation is a technique that can use several types of flour, such as wheat, rye, or other cereals, and water. The oldest process of sourdough preparation is spontaneous fermentation and acidification due to the local microbiota in a complex interaction, mainly lactic acid bacteria (LAB) and yeasts [1; 2; 3]. It is a traditional fermentation process used for various foods, especially baked goods, improving nutritional and health benefits [4; 5]. The best-known example is bread; however, other applications of this type of fermentation include biscuits, panetome, pasta, and beverages [6; 7; 8; 9]. Several varieties of this traditional sourdough bread recipe have appeared, bringing innovations to the primary process to attend to the bakery industry and the consumer, which appreciates nutritious, healthy, and tasty food [10]. Sourdough can be used by the bakery industry in several applications through liquid, pasty, or dry formats with live microorganisms. Moreover, there are \"ready-to-use\" sourdoughs with microorganisms inactivated to promote shelf-stable products [11].  
When sourdough is used, rather than _Saccharomyces cerevisiae_, a typical baker's yeast, for the bread dough fermentation, superior properties in bread quality and technological value are achieved [12].  
Probiotics are safe for human consumption and can produce metabolites that positively influence gastrointestinal [13] and bone health diseases [14]. Beneficial effects in eczema [15], allergies [16], respiratory tract infection [17], obesity [18], and cognitive and mental health are also described [19]. Probiotics act as gut microbiota, stimulating a complex communication with the body that is mediated by the metabolites generated [20]. Some LAB and yeasts present in sourdough fermentation are considered and presumed to be probiotics [21]. However, a complete analysis, such as a whole-genome analysis, is proposed for better probiotic characterization [22; 23; 24; 25; 26].  
This study aims to review improvements in sourdough fermentation, its enzymes, metabolites, and technological advances to produce sourdough bread with desirable properties.
## Appendix B
In general, \(\sigma_{1}^{2}\) and \(\sigma_{2}^{2}\) can be calculated as follows [10]:  
\[\sigma_{1}^{2}=\frac{1}{2C/N_{0}T_{coh}}\left(1+\frac{1}{2C/N_{0}T_{coh}}\right) \tag{11}\]  
\[\sigma_{2}^{2}=\frac{d}{4C/N_{0}T_{coh}}\left[1+\frac{2}{\left(2-d\right)C/N_ {0}T_{coh}}\right] \tag{12}\]  
where \(d\) is the early-late correlator spacing. Pre-filter-S usually has better performance than the popular error-state filter (pre-filter-E). Taking PRN25 for example, Figure 11 shows the differences between tracking errors obtained by pre-filter-E and pre-filter-S. According to statistic analysis, the RMS values of Doppler shift and code phase tracking errors corresponding to pre-filter-E are respectively 1.028 Hz and 1.62 \(\times 10^{-}\)\({}^{2}\) chip. For pre-filter-S, these two values are 0.487 Hz and 1.55 \(\times 10^{-}\)\({}^{2}\) chip.
## 5 Predictive Simulation and Analysis
### Support Vector Machine (SVM)
#### 5.2.1 SVM Basic Principles  
The SVM basic model is defined as a linear classifier, with the largest interval on the feature space. For a given training sample set \  
where, \(\xi_{i}\) is the non-negative relaxation variable introduced to measure the classification error, \(w\) is the normal vector of the hyperplane, and \(C\) is the penalty factor of the error term \(\sum_{i}^{n}\xi_{i}\).  
To solve the constrained optimization problems, a Lagrange function is introduced:  
\[L(w,b,\lambda)=\frac{1}{2}w^{T}\cdot w+C\sum_{i=1}^{n}\xi_{i}-\sum_{i=1}^{n} \lambda_{i}[y_{i}(w^{T}x_{i}+b)-1+\xi_{i}]-\sum_{i=1}^{n}\alpha_{i}\xi_{i}, \tag{11}\]  
where \(\lambda_{i}\geq 0,i=1,2,\cdots,n\). The dual problem of the Equation (10) is:  
\[\max Q(\lambda)=\sum_{i=1}^{n}\lambda_{i}-\frac{1}{2}\sum_{i=1}^{n}y_{i}y_{i} \lambda_{i}\lambda_{j}\left(x_{i}\cdot x_{j}\right), \tag{12}\]  
Finally, the optimal classification decision function is:  
\[f(x,\lambda)=sgn\left(\sum_{SV}y_{i}\lambda_{i}\left(x_{i}\cdot x_{j}\right)+ b\right), \tag{13}\]  
For the linear indivisible case, the dual problem is:  
\[\max Q(\lambda)=\sum_{i=1}^{n}\lambda_{i}-\frac{1}{2}\sum_{i=1}^{n}y_{i}y_{i} \lambda_{i}\lambda_{j}\left(\phi(x_{i})^{T}\cdot\phi\left(x_{j}\right)\right), \tag{14}\]  
where, \(\phi(x_{i})^{T}\cdot\phi\left(x_{j}\right)=\ K(x_{i},x_{j})\) is the kernel function.  
The corresponding optimal classification decision function can be obtained as follows:  
\[f(x,\lambda)=sgn(\sum_{SV}y_{i}\lambda_{i}K\left(x_{i},x_{j}\right)+b), \tag{15}\]  
In fact, the whole process is to transform a linear indivisible problem into a simpler high-dimensional linear separable problem, which depends on the kernel function of SVM, and is defined as: \(K\left(x_{i},x_{j}\right)=\phi\left(x_{i}\right)^{T}\cdot\phi\left(x_{j}\right)\).
# An NLOS Ranging Error Mitigation Method for 5G Positioning in Indoor Environments
###### Abstract  
Positioning based on wireless signals such as mobile communication networks has become an important means to provide high-precision location services in environments where satellite signals are blocked. In complex environments such as indoors and underground, wireless signal propagation is obstructed and non-line-of-sight (NLOS) phenomena appear due to serious occlusion and reflection. The time delay caused by NLOS effects has little impact on communication system but can significantly increase positioning errors in positioning systems. Therefore, the effective suppression of NLOS errors is crucial to improving 5G positioning accuracy. To address the insufficient feature extraction of existing NLOS error suppression methods, the neglect of residual NLOS measurement errors, and poor stability of position estimation results, this paper innovatively proposes an NLOS mitigation and location estimation method for 5G positioning terminals. Simulation and experimental test results demonstrate that the proposed method outperforms the comparative methods both theoretically and practically, achieving an average positioning accuracy of 1.85 m in complex indoor NLOS environments. The method proposed in this paper provides a new strategy for NLOS error suppression in indoor 5G positioning, which can significantly contribute to high-precision location services based on commercial 5G networks.  
5G positioning; Ziv-Zakai bound; non-line-of-sight mitigation; location estimation Article  
An NLOS Ranging Error Mitigation in Indoor Environments
## 6 Result and Discussion  
### Outline of Analysis and Results  
For the target building, the overall performance is investigated in terms of story drift and shear force. The cumulative damage index, plastic strain energy, cumulative and maximum ductility demands are used to investigate the progressive damage of BRBs. For this purpose, five BRBs are selected along with the height of the target building to examine the results. The selected BRBs that are located in the 2 nd, 5 th, 10 th, 15 th, and 19 th stories are denoted as BRB#1-#5, respectively, as shown in Figure 1. For the selected dampers, Table 7 summarizes the absorbed energy, the number of cycles, and strain amplitude obtained from the rainflow counting method after successive analysis of the five sets of lifetime multi-hazard scenarios. Since the average strain amplitude is higher for the damper installed in lower stories, the BRBs' contribution to absorb the MH energy becomes larger in response the accumulative damage increase. Table 7 also indicates that the energy absorption rate in upper stories is significantly smaller in comparison to the BRBs in lower stories.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Selected Damper**} & \multirow{2}{*}{**MH Scenarios**} & \multirow{2}{*}{**CDI**} & \multicolumn{2}{c}{**Strain Amplitude (\%)**} & \multirow{2}{*}{**Cycle**} & \multirow{2}{*}{**Energy**} \\ \cline{3-3} \cline{5-6}  & & & & **Ave.** & & **Max** & **No.** & **kN-m** \\ \hline \multirow{4}{*}{BRB\#1} & Set 1 & 0.643 & 0.048 & 1.210 & 129,094 & 25,400 \\  & Set 2 & 0.573 & 0.046 & 1.440 & 141,647 & 22,310 \\  & Set 3 & 0.501 & 0.042 & 1.018 & 138,566 & 21,310 \\  & Set 4 & 0.434 & 0.040 & 1.362 & 138,988 & 16,240 \\  & Set 5 & 0.363 & 0.043 & 1.092 & 126,927 & 13,120 \\ \hline \multirow{4}{*}{BRB\#2} & Set 1 & 0.105 & 0.031 & 0.800 & 128,968 & 3838 \\  & Set 2 & 0.074 & 0.029 & 0.783 & 140,602 & 3074 \\  & Set 3 & 0.087 & 0.028 & 1.067 & 136,815 & 3564 \\  & Set 4 & 0.060 & 0.026 & 1.214 & 138,187 & 2038 \\  & Set 5 & 0.043 & 0.027 & 0.844 & 126,173 & 1356 \\ \hline \multirow{4}{*}{BRB\#3} & Set 1 & 0.032 & 0.025 & 0.762 & 129,226 & 930 \\  & Set 2 & 0.026 & 0.024 & 0.758 & 139,249 & 751 \\ \cline{1-1}  & Set 3 & 0.027 & 0.023 & 0.754 & 135,340 & 932 \\ \cline{1-1}  & Set 4 & 0.024 & 0.021 & 0.701 & 137,201 & 654 \\ \cline{1-1}  & Set 5 & 0.019 & 0.022 & 0.780 & 126,453 & 461 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Cycle counting, strain amplitude, and CDI of selected BRBs under multi-hazard timelines.
## 3 Results and Discussion
### Bread Analysis
#### 3.3.3 Moisture Determination on Storage  
Figure 8 displays the moisture content values of the wheat flour bread and hemp protein breads on the 1 st and 4 th days of storage. As shown, the highest moisture content values were found in the breads with 15% hemp protein and the lowest for the control breads. These differences are probably related to the doughs' water absorption values. During baking, more water molecules were retained in the protein matrix, resulting in lower baking loss in the enriched bread. Upon storage, there were significant differences in the moisture content of the examined breads. In detail, all breads on the 4 th day of storage exhibited considerably lower values compared to the 1 st day of storage. The decrease in the moisture content of the bread crumb could be attributed to the migration of water from the crumb to the crust and to the retrogradation of starch [67, 68]. A similar observation was also reported by [PERSON] et al. [27] in the case of gluten-free breads.  
Figure 6: Images of wheat bread (0% hemp protein) and breads containing 5, 10, and 15% hemp protein.  
Figure 7: Mean values of specific volume of wheat flour bread (control, 0% of hemp protein) and of breads with 5, 10, and 15% hemp protein powder. Vertical bars display the 95% confidence intervals calculated from the error mean square of ANOVA. Groups with different letters have a statistically significant mean difference (\(p<0.05\)), based on [PERSON]’s multiple comparison test.
# MPCR-Net: Multiple Partial Point Clouds Registration Network Using a Global Template
## 5 Conclusions  
We designed a multiple partial point cloud registration network based on deep learning, called the MPCR-Net. MPCR-Net uses the global template point cloud converted from the CAD model of the workpiece to guide the registration of partial point clouds. All partial point clouds are registered to the global template point cloud through TPCC-Net and TMPE-Net in MPCR-Net, forming a fully registered point cloud of a workpiece. This can effectively reduce errors in multiple partial point cloud reconstruction.  
Experiment results demonstrated that MPCR-Net has the following advantages:  
1. Using a global-template-based multiple partial point cloud registration method can fully guarantee the overlap rate between each partial point cloud and its corresponding partial template point cloud, thereby reducing the registration error and improving the point cloud reconstruction accuracy.
2. Searching for correspondence points between partial point clouds and the global template point cloud through TPCC-Net does not require separate training for specific local data of point clouds, thereby effectively reducing the correspondence point estimation error.
3. The rigid body transformation matrix parameters in the registration are estimated through TMPE-Net, and estimation results are robust to changes in data points. It eliminates the shortcomings of other algorithms that cannot effectively register two point clouds with significant differences in the amount of data.  
Conceptualization, S.S. and H.Y.; Formal analysis, C.W.; Funding acquisition, J.Z.; Investigation, C.W.; Methodology, S.S.; Resources, C.W. and K.C.; Software, C.W.; Supervision, J.Z.; Validation, S.S.; Visualization, K.C.; Writing--original draft, S.S.; Writing--review & editing, H.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Excellent Youth Foundation of Jiangsu Scientific Committee of China (Grant No. BK20190103) and Jiangsu Government Scholarship for Overseas Studies (Grant No. JS-2018-258).  
The study data can be obtained by email request to the authors.  
The authors declare no conflict of interest.
# An Analog Circuit Fault Diagnosis Method Based on Circle Model and Extreme Learning Machine
###### Abstract  
The fault diagnosis of analog circuits faces problems, such as inefficient feature extraction and fault identification. To solve the problems, this paper combines the circle model and the extreme learning machine (ELM) into a fault diagnosis method for the linear analog circuit. Firstly, a circle model for the voltage features of fault elements was established in the complex domain, according to the relationship between the circuit response, element position and circuit topology. To eliminate the impacts of tolerances and signal aliasing, the 3D feature was introduced to make the indistinguishable features in fuzzy groups distinguishable. Fault feature separability is very important to improve the fault diagnosis accuracy. In addition, an effective classier can improve the precision and the time taken. With less computational complexity and a simpler process, the ELM algorithm has a fast speed and a good classification performance. The effectiveness of the proposed method is verified by simulation. The simulation results show the ELM-based algorithm classifier with the circle model can enhance precision and reduce time taken by about 80% in comparison with other methods for analog circuit fault diagnosis. To sum up, this proposed method offers a fault diagnosis method that reduces the complexity in generating fault features, improves the isolation probability of faults, speeds up fault classification, and simplifies fault testing.  
a _Article_  
An Analog Circuit Fault Diagnosis Method Based on Circle Model and Extreme Learning Machine
## 5 Discussion and Recommendations
### Implications Relevance to This Special Issue  
This research aimed to develop a predictive model for customer turnover in the telecommunications industry that could distinguish between customers who are likely to churn in the near future and those who are loyal to the company. The value of such a strategy to a firm is that it avoids wasting money on useless mass marketing approaches and allows organizations to target true churners by removing customers who are likely to churn. Furthermore, as explained in Section 1, obtaining a new customer costs eight times as much as keeping an existing one; as a result, because the churn predictive model can predict future churners, businesses seeking to keep their clientele can concentrate on retention strategies rather than acquisition strategies, which are less expensive.  
In addition, this study also improves the interpretability of customer churn predictive models through the use of LIME and SHAP to optimize their local and global interpretability. It provides decision-makers with an overall explanation of the factors affecting the customer's decision to churn, as well as a specific analysis for every single customer. As a result, decision-makers can more easily understand the results of advanced predictive models and use these explanations to develop global strategies and customize strategies for specific customer segments.  
The conclusions of this study have significant consequences for telecommunication companies. Apart from the notion of establishing a predictive system for predicting customer turnover in the telecommunication industry, the findings of this study may be applied to the banking industry in terms of developing a churn prediction model for debit card customers.  
To generalize the previous discussion, we may advocate that companies employ machine learning approaches to transform existing consumer information in their databases into meaningful information that can assist them with their efforts to market based on the research results. They would also benefit from using machine learning to create a projectedchurn model, which could act as an alert system for organizations and help them to spend their retention money effectively.
# Analysis of Quantum-Classical Hybrid Deep Learning for 6G Image Processing with Copyright Detection
## 4 Simulation Analysis
Figure 2:0 Classical image processing results in 6G network. networks, particularly in improving image quality while maintaining quantum methods' speed and efficiency advantages. Our simulations comparing quantum and classical approaches in the context of 6G networks have yielded intriguing and somewhat contrasting results. The initial simulations, focusing on general network optimization tasks, suggested that classical models outperformed quantum models in terms of execution time and cost-effectiveness. However, our latest simulation presents a different picture, specifically targeting image processing tasks within 6G networks. In the image processing context, as illustrated in Figure 7, the quantum model demonstrates superior performance in terms of execution time and cost-effectiveness. The quantum approach, implementing edge detection, consistently achieves lower execution times than the classical model's Gaussian blur method. This advantage becomes more pronounced as the quantum advantage increases. Similarly, the quantum model shows lower average processing costs, with the cost-effectiveness improving as quantum technology advances. However, the PSNR comparison in Figure 11 reveals an important nuance. Despite the quantum model's advantages in speed and cost, the classical model achieves significantly higher PSNR values (around 60) compared to the quantum model (around 2). This indicates that while the quantum approach is faster and more cost-effective, the classical method preserves more of the original image information. The nature of the tasks performed can explain this apparent contradiction. Edge detection, used in the quantum model, fundamentally alters the image more drastically than Gaussian blur, naturally leading to lower PSNR values. This highlights the importance of considering the specific requirements of the task at hand when choosing between quantum and classical approaches. In conclusion, our simulations suggest that quantum computing shows great promise for certain 6G network operations aspects, particularly in image processing tasks where speed and cost-effectiveness are paramount. The quantum model's ability to perform edge detection quickly and efficiently could be invaluable in applications requiring rapid image analysis or feature extraction. However, the classical model's superior performance in preserving image fidelity, as measured by PSNR, indicates that it remains highly relevant, especially for applications where maintaining image quality is crucial. This underscores the potential for a hybrid approach in future 6G networks, leveraging the strengths of both quantum and classical computing to optimize different aspects of network operations and image processing tasks. These findings emphasize the need for continued research and development in quantum computing for 6G networks, while also highlighting the importance of task-specific optimization and the potential benefits of integrating quantum and classical approaches in future network architectures.
## 4 Conclusions  
This study investigated the possibility of low temperature spray drying of buckwheat honey with the application of dehumidified air and its properties in comparison to traditional spray drying, which has not been done before for this type of honey. The findings of this study indicate the successful spray drying of all of the variants. The production of  
Figure 5: Dendrogram plot from HCA of obtained buckwheat honey powders.  
honey powders with high \(R_{p}\) (87% and 97% for variants DASDMD75 and DASDMP75, respectively) and, at the same time, with low carrier content (25%) was accomplished. This paper also describes the energy consumption analysis of dehumidified-air-assisted spray drying, which has not been presented in the literature before. This provides practical information for a possible implementation of this innovative technique in industry.  
Obtained results point to the statistically significant differences in the physiochemical properties of powders spray dried conventionally and at low drying temperatures. Powders produced with the application of dehumidified air showed smaller and more spherical particles with smooth surfaces in comparison to variants spray dried traditionally. Moreover, variants with skim milk powder as a carrier, in comparison to maltodextrin, were more spherical and less conglomerated and had particles that were less scattered. All of the buckwheat honey powders had low moisture content and water activity, below 5% and 0.3, respectively, which ensured their stability. It is worth underlining that powders with skim milk powder as a carrier had lower \(a_{w}\), which highlights the usefulness of MP as a carrier in buckwheat honey spray drying as it points to its better storage stability. It should also be emphasized that powders with skim milk powder as a carrier exhibited the highest antioxidant activity. The results presented in this paper provide further evidence of the validity of dehumidified-air-assisted spray drying and of skim milk powder as a carrier of buckwheat honey as an example material.  
Analysis, writing of the original draft, review and editing were done by [PERSON] The methodology and analysis were done by [PERSON] The conceptualization, methodology, review and editing were done by [PERSON] All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy restrictions.  
The authors would like to thank [PERSON] for participation in the experimental part of the research.