esa-sceva/satcom-chunk-collection huggingface dataset:
_animals_  
_Article_  
**Autochthonous Yeast from Pork and Game Meat**  
**Femented Sausages for Application in Meat**  
**Protection and Aroma Developing**  
**[PERSON] 1,*(), [PERSON] 2, [PERSON] 1, [PERSON] 1 and [PERSON] 1()**  
1 Department of Analytical Chemistry and Food Technology, University of Castilla La Mancha,  
13071 Ciudad Real, Spain; [EMAIL_ADDRESS] (M.A.); [EMAIL_ADDRESS] (M.A.-V.); [EMAIL_ADDRESS] (A.B.)  
2 Regional Institute of Applied Scientific Investigation (IRICA), University of Castilla-La Mancha,  
13071 Ciudad Real, Spain; [EMAIL_ADDRESS]  
* Correspondence: [PERSON]Received: 11 November 2020; Accepted: 7 December 2020; Published: 9 December 2020  
**Simple Summary:** Yeasts are microorganisms presented naturally in meat products microbiota, which carry out an essential role in the maturation process of fermented sausages. The maturation of this type of product could be better controlled by the addition of a starter culture or a mix of strains that could standardize the process in diverse factories. Moreover, it would be favorable to find cultures that, not only conduct the curing process, but also present an added value such as the meat product protection (antioxidant capability and biocontrol activity). Yeasts also have an important role in the meat product aroma development. Therefore, it would be interesting to select a pleasant-aroma forming strain in order to elaborate a future starter culture for fermented sausage maturation.  
**Abstract:** The wild yeast community was studied in fermented sausages from pork and game meat (deer and wild boar) during the maturation process from different curing rooms. Although the biotechnological importance of yeasts in the maturation process of pork sausages is known, there is a lack of information for sausage maturation involving game meat. A total of 123 yeasts were isolated and, by amplifying and sequencing of the ITS region, were classified in 14 species. _Deharyomyces hansenii, Kazachstania seruzzii_, and _Wickerhamomyces anomalus_ were isolated in both pork and game samples. The PCR-RAPD technique differentiated between 26 and 18 strains from pork and game meat sausages, respectively. The physicochemical parameters and their relationship with the yeast community were also studied. The antioxidant and anti-lipid peroxidation capability were analyzed and the 70% and 50% of the tested strains showed these abilities, respectively. Moreover, the biocontrol capability against mycotoxicingic molds was found in 19 strains, but better results were observed in game meat yeasts. On the other hand, almost 30% of strains produce a pleasant olfactory aroma, and volatile compounds associated with the yeast pathway metabolic during the maturation process have been characterized such as esters, aldehydes, fused alcohols, etc. This study has allowed a better understanding of the biodiversity of this type of food, as well as selecting potential yeast strains for their future use as starters.  
**Keywords:** pork fermented sausages; game meat fermented sausages; autochthonous yeast; antioxidant activity; biocontrol; aroma formation
# Research on the Applicability of Transformer Model in Remote-Sensing Image Segmentation
## 2 Methods
### Training Time Comparison of Models  
For the consumption of training time for the three transformer models, this study will compare the analysis on the basis of the training time of the three models in two data sets, namely the Vaihingen data set and Potsdam data set (Tables 1 and 2), with time in seconds.
# Uncertainty Detection: A Multi-View Decision Boundary Approach Against Healthcare Unknown Intents
## 5 Performance Evaluation and Result Analysis
### Comparative Experiment
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Accuracy** & **F1** & **F1-Known** & **F1-Unknown** \\ \hline LOF & 0.6866 \(\dagger\) & 0.5416 \(\dagger\) & 0.6042 \(\dagger\) & 0.0417 \(\dagger\) \\ \hline DOC & 0.7165 \(\dagger\) & 0.5871 \(\dagger\) & 0.6263 \(\dagger\) & 0.2740 \(\dagger\) \\ \hline DeepUnk & 0.7048 \(\dagger\) & 0.5392 \(\dagger\) & 0.5919 \(\dagger\) & 0.1179 \(\dagger\) \\ \hline ADB & 0.7735 \(\dagger\) & 0.7418 \(\dagger\) & 0.7870 & 0.3799 \\ \hline Ours & **0.7896** & **0.7565** & **0.8000** & **0.4082** \\ \hline \hline \end{tabular} Notes: \(t\)-test between our proposed method and benchmarks: \(\dagger\)\(p<\) 0.05.  
\end{table} 
Table 3: Detection results under the unknown intent ratio of 25%. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Accuracy** & **F1** & **F1-Known** & **F1-Unknown** \\ \hline LOF & 0.2047 \(\dagger\) & 0.2285 \(\dagger\) & 0.2514 \(\dagger\) & 0.1828 \(\dagger\) \\ \hline DOC & 0.2243 \(\dagger\) & 0.2305 \(\dagger\) & 0.2370 \(\dagger\) & 0.2177 \(\dagger\) \\ \hline DeepUnk & 0.1752 \(\dagger\) & 0.2105 \(\dagger\) & 0.2426 \(\dagger\) & 0.1466 \(\dagger\) \\ \hline ADB & 0.5058 & 0.4224 & 0.3424 & 0.5823 \\ \hline Ours & **0.5213** & **0.4477** & **0.3742** & **0.5948** \\ \hline \hline \end{tabular} Notes: \(t\)-test between our proposed method and benchmarks: \(\dagger\)\(p<\) 0.05.  
\end{table} 
Table 5: Detection results under the unknown intent ratio of 75%. In the 25% unknown intent ratio scenario, Figure 4 shows that the full multi-view combination achieves the highest scores across all evaluation metrics, demonstrating that multi-view fusion outperforms single-view or dual-view cases when the unknown intent proportion is relatively low. For the 50% and 75% ratios, the combination of UV and SDV performs comparably to the full multi-view approach, with both configurations surpassing other cases in every metric. The incorporation of SDV yields consistent improvements in F1-unknown across all ratios, which verifies that intent label design information effectively enhances unknown intent detection. MEV shows its most pronounced impact at the 25% ratio. However, its contribution diminishes at higher unknown intent ratios, suggesting that knowledge graph constraints may become less effective in high-noise environments.
###### Abstract  
The doubly-fed induction generators (DFIGs) integrated to the grid through the voltage source converter-high voltage direct current (VSC-HVDC), the cascaded droop control from the system frequency to the DC voltage, then to the active output of the DFIG, was applied to enhance the frequency regulation capability of the power system. The improved dynamic power flow (DPF) model was newly proposed to quantify the frequency response of the coordinated regulation with the inertia of the VSC-HVDC and the DFIGs, and the primary regulation of the synchronous generators (SGs) and the DFIGs. New features of the proposed model include: (i) the SGs' output in the DPF considering the governor delay, (ii) setting of the virtual inertia of the VSC-HVDC within the DC voltage constraint, and (iii) variable inertia of the DFIGs following changing the kinetic energy of the rotor. The numerical results show the feasibility of the proposed model, and validate the regulation effect and accuracy of the modified inertia of the DFIGs and the maximized virtual inertia of the VSC-HVDC.  
dynamic power flow; doubly-fed induction generator (DFIG); voltage source converter-high voltage direct current (VSC-HVDC); frequency regulation; inertia constant; governor delay +
Footnote †: journal: Applied Physics Letters  
1
## 2 Background  
### Bike Sharing Systems  
All bike sharing systems operate on the basis of a common philosophy, their principle is simple: individuals use bicycles on an \"as-needed\" basis without the costs and responsibilities that owning a bicycle normally entails [9]. Figure 1 shows a classical bike station, where there are free docks and available bikes to rent within a station map of Madrid's Bike Sharing System called BiciMad. As shown on the map, the bike sharing system offers a wide list of stations located across the centre of the city.  
[PERSON] indicates in his study [3] that bike sharing systems can be categorized into 4 generations depending on their features: (1) First generation bike sharing systems: the first generation of bike sharing systems was introduced in Amsterdam (1965), La Rochelle (1976) and Cambridge (1993). These systems provided totally free bicycles which could be picked and returned in any location. The vast majority of these systems were closed due to vandalism. (2) Second generation: this generation tries to solve the drawbacks of the previous one. In this case, the systems had a coin deposit (like the supermarket trolleys) but they still suffered from thefts due to the anonymity of the users. (3) Third generation: this generation uses high tech solutions including electronic locking docks, smart cards, mobile applications, built-in GPS devices in the bikes and totem applications. (4) Fourth generation: it is still in the process of development, this generation includes movable docking stations, solar-powered docking stations, electric bikes and real-time system data.  
This last generation of systems includes a new scheme named dock-less bike sharing solution which reuses the first-generation philosophy: free bicycles around the city. However, in this case, they are equipped with a GPS tracker and they can be found and rented through a mobile application. This kind of systems are quite spread nowadays in China and they are arriving to citieslike Singapore, Cambridge and Seattle through enterprises as OFO [10], Mobike [11] and Bluegogo. Nevertheless, these bike sharing systems may suffer from vandalism and other problems already presented in first-generation. Some of these big enterprises--such as Bluegogo [12] in China--has recently gone bankrupt due to financial problems.  
Furthermore, these bike sharing systems present other kind of operating diagram that varies from the one based on stations. In consequence, there are huge differences regarding to the data processing. This work focuses on systems based on stations which will be deeply discussed.  
Concerning Third and Fourth generations of BSS, there are some common issues that will be described in detail in the following section.
# A Hybrid Brain Storm Optimization Algorithm to Solve the Emergency Relief Routing Model
## 5 Solving the ERRP Model Based on the Proposed Hybrid Algorithm
### SA Selection  
In the LNS operation, individuals whose objective function values are less than the original are retained. To broaden the search space covered by the algorithm in the solution space, we add the SA selection step to the HBSO algorithm. Individuals whose objective function values are bigger than the original individuals are retained according to the principle of SA. In the first half of the iterations, the obtained individuals are not good enough, and the SA selection is not performed. In the second half of the iterations, the non-better individuals are selectively retained based on the probability. According to Metropolis guidelines, the probability of maintaining a non-optimal new individual is dynamic, calculated as in Equations (24) and (25), where \(f_{1}\) and \(f_{2}\) are objective function values before and after repair, \(T\) is the initial temperature, and the temperature decrease after each iteration is measured by the coefficient \(K\). Therefore, the HBSO algorithm explores the solution space more comprehensively, and the final individuals obtained could be better. The pseudocode of the SA selection in the HBSO is shown in Algorithm 7.  
\[delta=\frac{f_{2}-f_{1}}{f_{1}} \tag{24}\]  
\[p_{select}=\exp\left(\frac{-delta}{KT}\right) \tag{25}\]  
```
Input: individuals after \"repair\", \(p_{select}\) (the probability of retaining non-optimal new individuals), \(f_{1}\) (the objective function value of the unrepaired individual), \(f_{2}\) (the objective function value of the repaired individual), \(gen\) (the current iteration number), \(max\_gen\) (the maximum number of iterations)
1 SA Selection: Selecting whether to retain new individuals based on the probability
2 ift\(f_{1}>f_{2}\)then
3 if\(gen>0.5*max\_gen\)then
4 Calculate the probability of retaining a new individual \(p_{select}\);
5 Create a value \(r_{select}\) at random in the range [0,1);
6 if\(r_{select}<p_{select}\)then
7 Retain the new individual as the solution for this generation;
8 else
9 Retain the original individual as the solution for this generation;
10 endif
11 endif
12 endif Output: the current optimal individual
```  
**Algorithm 7**The SA Selection Strategy
# An Automatic Navigation System for Unmanned Surface Vehicles in Realistic Sea Environments
## 3 The Static Environment Modeling and Path Planning
### The Realistic Sea Environment Model  
A sea area in Dalian harbor, which as a non-freezing deep-water harbor is one of the busiest harbors in China, was selected as the sea environment modeling area. Its electronic chart and binary map are shown in Figure 8a,b. The coastlines and ocean depth are clearly marked in the electronic chart, so it is clear that both collision area and safe area can be identified. In the electronic chart, the pixel values of the collision area are replaced by 0. The collision area is shown as black. The safe areas are replaced by the value 1 shown in white. The final map is the binary map that has only two possible values (0 or 1) for each pixel. The dimensions of the binary map are 1253 \(\times\) 1794 pixels with the 1 pixel = 1 m scale. The results from applying the steps of TFMS method with \(Saturation\) 0.9 are shown in Figure 8c-f for the real sea area.  
Figure 6: Path planning with the fast marching square (FMS) method. (**a**) The safety potential map \(V\); (**b**) The arrival time matrix \(T\); (**c**) Path obtained after applying gradient descent on \(T\); (**d**) Generated path and arrival time matrix \(T\) in three dimensions.  
Figure 7: Path planning with different \(Saturation\) values. (**a**) \(Saturation\) 0.9; (**b**) \(Saturation\) 0.7; (**c**) \(Saturation\) 0.5; (**d**) \(Saturation\) 0.3.
## 4 Numerical Simulation
Given that the DD-MZM optical input on scheme C4 is only 25% of the LD total power, the LD power in this circuit is first divided into 4 parts using a 1 \(\times\) 4 optical splitter (OS). Therefore, 1 OS output is used as the MZM optical input, and 3 OS outputs are combined using the 3 \(\times\) 1 optical combiner (OC). The output of 3 \(\times\) 1 OC is then recombined with the output of MZM by means of 2 \(\times\) 1 OC. For scheme C4, the bias voltage 2 of DD-MZM is regulated at 3 volts, and the value of the phase shift of EPS is set at 48\({}^{\circ}\). The power measurement in this simulation is identical to the measurement on links with ODSB and OSSB modulations. Figure 13c illustrates a simulation circuit to measure the C/N penalty of the RoF link with an irregular \(\theta\) for scheme C4. The components used in this circuit are no different from the simulation circuit employed for measuring the C/N penalty of the RoF link with an irregular \(\theta\) for scheme C2. However, this simulation uses MZM optical input by 25% from the LD total power; hence, MZM is given an input of 3 \(\times\) 1 OC output. An OS output is then recombined with the output of MZM using 2 \(\times\) 1 OC. For scheme C2, bias voltage 2 of MZM is also set at 3 V. The comparison result between \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Units** \\ \hline User defined reference wavelength & Checked & - \\ Reference wavelength & 1550 & nm \\ Length & 0–5 & km \\ Attenuation effect & Unchecked & - \\ Group velocity dispersion & Checked & - \\ Third-order dispersion & Unchecked & - \\ Frequency domain parameter & Unchecked & - \\ Dispersion & 17 & ps/nm/km \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Setting parameter of Optical Fiber. the mathematical model and simulation model has very good agreement, which shows the validity of the proposed method, as shown in Figure 14.  
The C/N penalty of the RoF is linked with ODSB modulation, and both the calculation result and simulation show a deep fade at \(L=1\) and \(3.1\) km. However, deep fade does not occur in the C/N penalty of the RoF link with OSSB modulation, although there is still a \(\Delta\) C/N penalty of \(2.5\) dB. The \(\Delta\) C/N penalty in the C/N penalty of the RoF link curve with an irregular \(\theta\) for scheme C4 was very small.
## 2 Materials and Methods
### Materials  
A total of 23 pesticide standards were used for analysis. The selection of pesticides was based on an assessment of the frequency of use in Austrian viticulture, the control of popular but no longer registered pesticides, and the use of various active compounds from the groups of insecticides, fungicides and herbicides. Table 1 shows the chemical group, the active substance, the molecular formula, molar mass, common commercial names of the pesticides and the legal limit for wine grapes. Analytical standards were purchased from LGC Standards (Teddington, UK) and Sigma Aldrich (St. Louis, MO, USA). All standards had the highest purity available (purity > 97% (exception: idoxacarb: purity 93.6%)). The internal standard used was triphenyl phosphate, which also was purchased from Sigma Aldrich (St. Louis, MO, USA). Acetonitrile (Sigma Aldrich Company, St. Louis, Missouri, MO, USA) and ethanol (99% (AustrAlco Osterreichische Alkoholhandels-GmbH, Spillern, Austria)) were used for the stock and dilution solutions.  
Eight fining agents were compared during the course of the experiments. Table 2 shows the product names, substance class and manufacturer. Furthermore, the salts sodium chloride, trisodium citrate dihydrate, disodium citrate sesquihydrate, magnesium sulfate (all Sigma Aldrich, St. Louis, MO, USA), Chromabond Diamino (Macherey-Nagel, Duren, Germany) and the solvents ethyl acetate (99.9% (Fluka, Buchs, St. Gallen, Switzerland, now Sigma Aldrich)), acetone (>99.5%) and formic acid (98 to 100%) (Sigma Aldrich, St. Louis, MO, USA) were used for QuECherS sample preparation.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Chemical Group**} & \multicolumn{2}{c}{**Active Substance**} & \multirow{2}{*}{**Application Group**} & \multirow{2}{*}{**Chemical Formula**} & \multicolumn{2}{c}{**Molecular**} & \multicolumn{2}{c}{**MR1s [mg/kg]**} & \multirow{2}{*}{**Commercial Name in Wine Industry**} \\  & **investigated Residue** & & & & & & \\ \hline
**Acetylalanine** & Benaliy1 & Fungicide & C\({}_{3}\)H\({}_{2}\)NO\({}_{3}\) & 335.40 & 0.3 & Caliben M.\({}^{+1}\) \\
**Carboundes** & Boscal1 & Fungicide/Botryicide & C\({}_{3}\)H\({}_{2}\)Co\({}_{3}\) & 343.21 & 5.0 & Flain WG, Collis, Cantro, Flain WG \\ \hline
**Thiephosphate acid ester** & Chlor
## 3 Case Study
### Results
#### 3.3.1 Pareto Frontiers
On each Pareto frontier, the single-objective optimal solutions are specifically denoted as points A and B, while the multi-objective compromise solution is marked as point C. Among them, schemes A and B represent extreme scenarios: Scheme A achieves the highest level of thermal comfort at the expense of the highest cost, whereas Scheme B minimizes economic expenditure but yields relatively lower comfort. Across all three typical days, a clear trade-off relationship between comfort and cost is observed--enhancing indoor thermal comfort inevitably leads to increased equipment operation costs. Table 4 presents the scheduling objectives under three optimization scenarios: maximum comfort, minimum cost, and the compromise solution selected using the TOPSIS method. As shown, on three typical days, when the indoor environment was optimized to the maximum comfort level, the operating cost exceeded the optimal cost value by at least 28.5%. On the contrary, when the operating costs were reduced to the minimum, the changes in temperature and humidity increased by at least 35.13% respectively compared to their respective optimal comfort levels, which was detrimental to the economic benefits of maintaining the system's operation. In contrast, the multi-objective optimization model achieves a balanced performance: although its economic cost and comfort are slightly inferior to those of the single-objective optimal solutions, it effectively harmonizes operational economy with indoor environmental comfort. Figure 6: Pareto optimal chart of three typical days: (**a**) typical day in summer; (**b**) typical day in transitional season; and (**c**) typical day in winter. Compared with the model optimized solely for users' comfort, the multi-objective optimization model improves economic performance under typical summer, transitional, and winter conditions by 26.5%, 14.9%, and 15.1%, respectively. In contrast, compared with the cost-optimal model, it reduces spectators' discomfort under the same three typical conditions by 10.0%, 39.3%, and 28.9%, respectively. Therefore, the multi-objective optimization model demonstrates a superior ability to balance economic efficiency and users' comfort, outperforming single-objective models under varying climatic scenarios.
# Electrospinning of High-Performance Nanofibres: State of the Art and Insights into the Path Forward
## 3 Electrospinning of High-Performance Polymers
### Pbo
However, the low molecular weight of the precursor compromised the mechanical strength of the resultant nanofibres [24]. Combinations of both the high molecular weight OH-PAA and the inexpensive DMOBPA were also used to produce an affordable PBO nanofibre without compromising the mechanical strength [24]. The synthesized precursor, methoxy-containing polyamic acid (MeO-PAA), had a higher intrinsic viscosity than OH-PAA or MeO-PA and thus an increased molecular weight. MeO-PAA was electrospun onto a rotating disk collector and then heat-treated to form the ring structure of PBO. However, a direct transformation to PBO was not observed. The MeO-PAA was converted to methoxy-containing polyimide (MeO-PI) at 350 \({}^{\circ}\)C and to poly(imide-_co_-benzoxazole) (PI-_co_-PBO) at 450 \({}^{\circ}\)C and 500 \({}^{\circ}\)C. It is suspected that more energy is required to fully convert MeO-PI into PBO, but this was not investigated in the study. The PI-_co_-PBO nanofibres were tested for tensile strength and thermal resistance. The nanofibres demonstrated thermal resistance above 500 \({}^{\circ}\)C, but the thermal decomposition temperature was approximately 50 \({}^{\circ}\)C lower than that of PBO nanofibres directly spun from the OH-PAA precursor. The tensile strength of the PI-_co_-PBO nanofibrous belts was the highest after a heat treatment at 450 \({}^{\circ}\)C, which resulted in a strength of 559 MPa. Compared to the tensile strength of other high-performance filmsand fibres, including m-aramid, pure PBO, and multiwalled carbon nanotubes, the tensile strength of PI-_co_-PBO was the highest that had been observed. The thermomechanical properties of the PI-_co_-PBO were slightly lower than that of the pure PBO nanofibres, with only a 76% modulus retention at 300 \({}^{\circ}\)C. However, the nanofibres were still sufficiently heat resistant and better maintained their strength and modulus at high temperatures when compared with other nanofibres. In summary, the literature reflects a variety of methods to electrospun PBO from precursors, with good consideration of how each precursor type affects both the performance and scalability of the process [24; 25; 59]. Given the high temperature resistance and excellent mechanical properties that have been displayed by PBO nanofibres, this type of nanofibre would be suitable for several applications where high heat and durability are primary concerns. Investigations into the porosity and filtration performance of PBO, as well as the potential for composite PBO nanofibre materials, will be interesting paths forward to progress the research on PBO and determine suitable applications.