esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Probiotic Properties of LAB  
LAB strains were subjected to analyze the acid and bile acid tolerance at a very low pH range. The isolated strain displayed the ability to survive in low acidic conditions for 2 h and 4 h. The survival ability (%) at low pH and bile salt concentrations were described in Figure 2a,b. Among three isolates, the isolate AS22 showed 89 \(\pm\) 1% survival rates in acidic conditions and showed 88 \(\pm\) 1% survival rates at 0.3% bile salt concentration. Fermented food consists of various LAB and protected the host organism by the production of various vitamins, stimulate the metabolism and ingestion in the intestine [28]. Acid tolerance is the main criteria to be considered as an important feature for the selection of a potent probiotic strain. The findings represented that AS22 effectively survived in the extreme pH (low pH) condition. In the case of _Lactobacillus_, the ability to survive in lower pH in the medium is depends on the intracellular enzyme activity. Glucose plays a potent role in enhancing the intracellular pH of the strain [29], thereby increasing the survival rate of LAB at a very low pH value. The acid tolerance potential of bacteria was mainly based on the outer membrane properties in stress conditions. The isolate AS22 showed high acid tolerance potential at low pH which was significantly concurrent with [PERSON] et al., results, that stated as _Lactobacillus salivarius_ TUCO-L2 had significant resistance to low pH and high bile salts concentrations [30].  
Figure 2: **(a)** Survival rate of LAB isolated at low pH (2.0) after 2 and 4 h incubation. The strains were cultured in MRS medium at 28 \(\pm\) 2 \({}^{\circ}\)C and survival rate was expressed as percentage; **(b)**. Survival rate of LAB isolates at 0.3% bile salt concentration after 2 and 4 h incubation. The strains were cultured in MRS medium at 28 \(\pm\) 2 \({}^{\circ}\)C and survival rate was expressed as percentage.  
Among the isolated bacterial strains, AS22 showed higher resistance properties in bile salt condition than the other isolates. The isolated strain was survived at 0.3% bile salt concentrations and the results were described in Figure 2b. Tolerance of LAB at low pH and at high concentration of bile salt is the prerequisite for the screening of probiotic organisms. The stress-tolerant probiotic organisms secrete bacteriocin and various organic acids [16]. The isolate AS22 showed maximum survivability in an acidic environment and bile salt (>80%). In the present study, AS22 significantly survived in the low pH and bile salts environment, our data were strongly concurrent with [PERSON] et al. and [PERSON] et al., they have reported that the _L. salivarius_ SMXD5 [31], _L. salivarius_ UCO-979C-1 [32] had the significant survival rate at low pH and bile salts environment. Bile salts play a significant role in the non-specific and specific defense system in the gastrointestinal tract. In general probiotics must tolerate and survive in numerous environmental factors including bile salt related stress, low pH and nutrient limitations [33].
# Optimal Configuration and Path Planning for UAV Swarms Using a Novel Localization Approach
## 2 Problem Formulation
### Measurement Variance Model with Distance-Dependent Noise  
Considering the influence of signal frequency, bandwidth, response time, and SNR, the CRLB of the TOA measurement error variance \(\sigma_{i}^{2}\) can be represented as [23]:  
\[\sigma_{i}^{2}=\frac{c}{\tau\cdot SNR_{i}\cdot F(f_{0},B)}, \tag{5}\]  
where, \(\tau\) is the observation time, \(f_{0}\) is the center frequency, \(B\) is the bandwidth of the received signal, and \(c\) is some constant. High accuracy can be achieved by utilizing high-precision time of arrival measurement techniques at reasonable SNR levels. With constant emitter power and constant frequency, the variance of time-delay measurement is inversely proportional to the SNR, and the SNR is inversely proportional to \(r^{2}\). Therefore, the relationship of the \(i\)-th receiver error and distance can be expressed as [24]:  
\[\sigma_{i}^{2}(r)=\left\{\begin{array}{ll}\frac{a}{SNR_{0}}\cdot\frac{r_{1}^ {2}}{r_{0}^{2}}&r_{i}>r_{0}\\ \frac{a}{SNR_{0}}&r_{i}\leq r_{0}\\ \end{array}\right., \tag{6}\]  
where \(r_{0}\) is the lower bound of the distance corresponding to the minimum of TOA error variance and \(SNR_{0}\) is the corresponding optimal SNR at the shortest distance.  
The parameter-dependent standard deviation is more complex compared with the constant deviation, so the CRLB and field of view is changing in TDOA localization. Hence, the parameter-dependent standard deviation must be taken into account for accuracy analysis.  
The problem of emitter localization is to estimate the location more precisely. In this paper, we mainly study the optimal sensor configuration, which can provide two basic rules to understand the rules to improve the localization performance. Then the online sensor management problem of optimal UAVs trajectories is explored.
## 2 Materials and Methods
### Simulation Model  
When using the SSR estimation model based on multi-angle SG, the remote-sensing image information under two SVA is first obtained, and then SSR is estimated by the model. Without considering inherent errors of the sensor and the error of radiation correction, accurate SG radiance of different observation geometries can always be obtained, thus providing accurate SSR information. In fact, the above two kinds of errors always exist, and thus different imaging geometry combinations involve different transmission processes for errors. We establish an error transfer simulation model, as shown in Figure 2. We use python programming language to design simulation software program based on the above models, and analyze the error transmission situations under different imaging geometry combinations to evaluate the optimal observation geometry combination by the program.  
The process is explained as follows:  
1. Input model parameters and determine sensor pointing angle (\(P\)), scene orientation angle (\(S\)), sun zenith angle (\(\theta_{0}\)), sun azimuth angle (\(\phi_{0}\)), and SVA combination (\(\theta_{I}\),\(\theta_{II}\));
2. Input the sea surface wind speed (\(W\)), use the CM model [1] to calculate SSR attributable to sea surface wind (\(\sigma_{0}^{2}\)), and use the SSR as the real value of the assessment;
3. Combine the sensor information input in 1, calculate sensor azimuth angles (\(\phi_{I}\),\(\phi_{II}\)) under SVA combinations (\(\theta_{I}\),\(\theta_{II}\)) according to Equations (14), (15), (17) and (18);
4. Simulate a pair of normalized SG radiance (\(I^{\prime}\), \(I^{\prime\prime}\)) (Equation (9));
5. Add the simulated error (\(\Delta I\)) to the normalized SG radiance (\(I^{\prime}\), \(I^{\prime\prime}\)). Here, we use a 5% multiplicative error and an additive error of 0.00005 according to the signal-to-noise ratio of the ocean optical remote sensor and the general radiation correction error (\(\Delta I=I*5\%+0.0005\)), and get a pair of normalized SG radiance with errors (\(f(I^{\prime})\), \(f(I^{\prime\prime})\)).
6. Apply \(f(I^{\prime})\), \(f(I^{\prime\prime})\) and related parameters to the SSR estimation model based on multi-angle SG (Equation (12)). Then get the estimated SSR (\((\sigma_{0}^{2})\));
7. Calculate the error (\(\Delta\sigma_{0}^{2}\)) between the estimated SSR (\((\sigma_{0}^{2})\)) and the real SSR (\(\sigma_{0}^{2}\)) attributable to wind speed.  
The result obtained (\(\Delta\sigma_{0}^{2}\)) using the above model is the result of the SG radiance error transmitted in the SSR estimation model. In this paper, the expression of \(\Delta\sigma_{0}^{2}\) uses the percentage error of \((\sigma_{0}^{2})^{\prime}\) and \(\sigma_{0}^{2}\). The smaller \(\Delta\sigma_{0}^{2}\), the better the SVA combination under such environmental factors (sun zenith angle, sun azimuth angle, wind speed). Conversely, if the value of \(\Delta\sigma_{0}^{2}\) is large, this combination should be avoided.
## 2 Materials and Methods
### Synthesis of Potassium ((2-(1H-imidazol-1-yl)ethyl) sulfonyl)-(trifluoromethane sulfonyl)imide  
In a round-bottom flask equipped with a condenser, imidazole (1.09 g, 159.0 mmol) and potassium vinyl-STFSI (3.00 g, 10.6 mmol) were dissolved in 40 mL of methanol. The solution was stirred under reflux for 24 h. After the reaction, the solvent was removed by a rotary evaporator and the solid residue was then washed with 50 mL of dichloromethane. The resulting solid was filtered, rinsed with 20 mL of _n_-pentane and dried under vacuum to give the final product as a beige solid (mass = 2.8 g, yield = 76%). HRMS analysis of C6H7N3O4S2F3K: calculated m/z = 305.9821 [M-K+]cal, experimental m/z= 305.9813 [M-K+]exp. 1H NMR (400 MHz, DMSO-d6, \(\delta\) ppm): 7.64 (s, -N=CH-, 1H); 7.20 (t, =CH-N<, 1H); 6.86 (t, =N-CH=CH, 1H); 4.31 (t, >N-CH2-CH2, 2H); 3.44 (t, CH2-CH2-SO2, 2H). 19F NMR (376 MHz, DMSO-d6, \(\delta\) ppm): -77.49.  
Synthesis of ((2-(3-(cyanomethyl)-1H-imidazol-3-ium-1-yl)ethyl)sulfonyl) ((trifluoromethane sulfonyl))imide (Called **ZN**)  
To obtain the zwitterion bearing STFSI anion, potassium Im-2C-STFSI (2.00 g, 5.7 \(\times\) 10\({}^{-3}\) mol) and chloroacetonitrile (2.21 g, 28.7 \(\times\) 10\({}^{-3}\) mol) dissolved in 10 mL of acetonitrile were loaded in a 50 mL round-bottom flask equipped with a condenser. The solution was stirred under reflux overnight. After the reaction, the solvent was removed by a rotary evaporator and the solid residue was then washed by stirring with 50 mL of dichloromethane for 1 h. The resulting solid was filtered, rinsed with 20 mL of dichloromethane and dried under vacuum to give the final product as a beige solid (mass = 1.3 g, yield = 65%) (Scheme 1). HRMS analysis of C8H9N4O4S2F3: calculated m/z= 347.0090 [M + H+]cal, experimental m/z = 347.0080 [M + H+]exp. 1H NMR (400 MHz, DMSO-d6, \(\delta\) ppm): 9.36 (s,-N=CH-N<, 1H); 7.92 (t =CH-N<, 1H); 7.89 (t, =N-CH=CH-, 1H); 5.59 (s, NC-CH2-N, 2H); 4.61 (t, >N-CH2-CH2, 2H); 3.61 (t, CH2-CH2-SO2, 2H). 19F NMR (376 MHz, DMSO-d6, \(\delta\) ppm): -77.54.
## 5 Conclusions  
We designed a heterodyne laser interferometer for measuring the optical path difference of a steering mirror. The optical elements are fixed to the optical substrate through UV-glue bonding to reduce thermal noise. In a vacuum (10\({}^{-3}\) Pa), vibration isolation (up to 1 Hz), and temperature-controlled (10 mK) experimental environment, the measurement accuracy is increased by about four orders of magnitude through the common-mode suppression scheme and achieves 390 pm\(/\sqrt{\mathrm{Hz}}\) in the 1 mHz to 1 Hz frequency band. It cannot be used directly to measure the optical path difference of a steering mirror driven by a minor variation in voltage because the measurement accuracy is higher than the target. Our analysis suggests that the measurement performance of the interferometer can be improved by improving the temperature control accuracy and increasing the vacuum level. Finally, through analogy in the frequency domain, we validated the TTL noise model and obtained that the optical path difference caused by the steering mirror is less than 5 pm\(/\sqrt{\mathrm{Hz}}\) in the 1 mHz to 1 Hz frequency band. It met the optical path difference requirement of 8 pm\(/\sqrt{\mathrm{Hz}}\). Our research achievements are at the forefront globally, with no orders of magnitude difference compared to existing studies, both domestically and internationally.  
Conceptualization, W.Z.; methodology, W.Z.; software, Y.X.; validation, W.Z., Y.G., X.Q. and X.W.; investigation, W.Z. and Y.G.; resources, J.J., L.M. and Y.X.; data curation, W.Z. and Q.J.; writing--original draft preparation, W.Z.; writing--review and editing, W.Z. and L.M.;  
Figure 11: Frequency-domain results of the optical path differences.  
supervision, J.J., Y.X. and L.M.; project administration, J.J. All authors have read and agreed to the published version of the manuscript.
* National Key R&D Program of China (2021 YFC2201804 and 2022 YFC2203701), National Natural Science Foundation of China (12103014).
* Not applicable.
* Data are contained within the article.
* This work was completed at the Shanghai Institute of Technical Physics, Chinese Academy of Sciences, and we would like to thank all the accommodating and helpful staff of this institute.
* The authors have declared no potential conflict of interest with respect to the research, authorship, and/or publication of this article.
## 3 Results and Discussion
### Effect of Cyanide on AMO, NaR, and NiR  
Enzyme inhibition can occur in different ways such as the binding of an inhibitor onto an active site or for an example a site of the AMO responsible for NH\({}_{4}\)-N oxidation. The second inhibition mechanism can involve the removal of the Cu co-factor of AMO which in turn affects the activity of AMO [24]. To assess the effect of CN\({}^{-}\) towards SNaD, enzyme extracts from _A. courvalini_ cultivated in batch cultures were used. The effect of CN\({}^{-}\) towards the expression of NH\({}_{4}\)-N (AMO), NO\({}_{3}\)-N (NaR) and NO\({}_{2}\)-N (NiR) oxidizing enzymes was evaluated (Figure 4A).  
The activity and presence of AMO, NaR, and NiR was conducted using cell-free extracts by the addition of the enzyme solution into solutions of NH\({}_{4}\)-N. The results revealed that _A. courvalini  
Figure 4: The activity of NH\({}_{4}\)-N, NO\({}_{3}\)-N and NO\({}_{2}\)-N oxidizing enzymes and CN\({}^{-}\) degrading enzyme. (**A**): Effect of CN\({}^{-}\) on the induction of NH\({}_{4}\)-N, NO\({}_{3}\)-N and NO\({}_{2}\)-N oxidizing enzymes by _A. courvalini_. (**B**): Effect of CN\({}^{-}\) on free cell NH\({}_{4}\)-N, NO\({}_{3}\)-N and NO\({}_{2}\)-N oxidizing enzymes.  
was able to express AMO, and not NaR and NiR; this was shown by the decrease of NH\({}_{4}\)-N and the accumulation of NO\({}_{2}\)-N; however, there was minimal NO\({}_{2}\)-N oxidation observed thus minimal NO\({}_{3}\)-N formation and accumulation detected (see Figure 4B). This observation led to a hypothesis that CN\({}^{-}\) inhibited the expression of NO\({}_{3}\)-N and NO\({}_{2}\)-N oxidizing enzymes or results in the excretion of non-active NO\({}_{3}\)-N and NO\({}_{2}\)-N oxidizing enzymes.  
In addition, the competitive inhibition by the addition of CN\({}^{-}\) to free cell extract was examined. This experiment aimed to determine the effect of CN\({}^{-}\) on the expressed NH\({}_{4}\)-N, NO\({}_{3}\)-N, and NO\({}_{2}\)-N oxidizing enzymes, of which the outcomes indicated that the presence of CN\({}^{-}\) did not have an effect on the NH\({}_{4}\)-N reducing enzyme; albeit, a slow decrease of NO\({}_{2}\)-N was observed which suggested the activation of denitrification. Additionally, no NO\({}_{3}\)-N accumulation was observed which meant that _A. courvulinii_ converted NO\({}_{2}\)-N directly to N\({}_{2}\).  
The activation of NO\({}_{2}\)-N genes when CN\({}^{-}\) was added to bacterial cultures, could be a confirmation that _A. courvulinii_ expresses an ANR protein in response to the presence of CN\({}^{-}\) as a protective mechanism for the cyanide-resistant bacteria used in this research in order to mitigate against the toxicity of the CN\({}^{-}\). Furthermore, the activation of this protein can lead to the induction of NAR, NIR, NOR and N\({}_{2}\)OR although this was not confirmed in this study [1].
# Path Planning of Inspection Robot Based on Improved Ant Colony Algorithm
## 3 Improved Ant Colony Algorithm
### Improved Pheromone Update Rule and Mathematical Analysis of Convergence
#### 3.4.4 Theoretical Proof of Convergence  
To demonstrate that the improved algorithm exhibits faster convergence compared to the original algorithm, we can consider the following points:  
1. Increased pheromone concentration differences: The improved pheromone update rule enhances pheromone levels for optimal paths while reducing them for suboptimal paths. This creates a greater disparity in pheromone concentrations among different paths. As a result, ants are more likely to select the optimal path during route selection, thereby reducing the randomness of the search process. This targeted exploration effectively accelerates convergence.  
2. Bellman equation and expected path length: According to the Bellman equation, the expected path length decreases with each iteration. In the improved pheromone update rule, the rapid adjustment of pheromone concentrations facilitates the repeated selection of optimal paths. This can be likened to the introduction of a reward function term in the Bellman equation, enabling the expected path length \(E[L]\) to decrease at a faster rate:  
\[E[L](t+1)<E[L](t)-\vartheta\cdot\frac{Q}{L_{min}}+\
u\cdot\frac{Q}{L_{max}} \tag{21}\]  
The reduction in the expected path length occurs at a significantly faster rate compared to the classical ant colony optimization algorithm. Consequently, under the same number of iterations, the improved algorithm converges more rapidly toward the optimal solution.
# Matching of Energy Provisions in Multihop Wireless Infra-Structures
## 7 Conclusions  
This paper has discussed a new energy provision approach for WMI networks. We investigate the special features of energy consumption in WMI networks that are powered with intermittent energy sources. In particular, we focus on energy consumption features during non-charging periods with polarized traffic flow toward the network gateway. An energy provision matching approach, called Matching-EP, is proposed to enable the matching between energy consumption and energy  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Items** & _Diff(z1)_ & _Diff(z2)_ & _Diff(z3)_ & _Diff(z4)_ & _Diff(z5)_ & **Accuracy** \\ \hline Time average value of simulation value \(Diff_{M}(i)\) result & 1.103 & 1.1211 & 1.1286 & 0.880 & 0.639 & 1 \\ _Diff(0)_ for uniform energy consumption & 1 & 1 & 1 & 1 & 1 & 0.833 \\ _Diff(0)_ from _TX-RX-OH_ analysis & 1.073 & 1.160 & 1.029 & 0.844 & 0.860 & 0.915 \\ \hline \end{tabular}
\end{table}
Table 8: The energy differentiation of BSs in zones and the accuracy of differentiation analysis in Grid WMI. (The related setup: short path routing, \(K_{OH}=3\) in _TX-RX-OH_ relation based analysis).  
Figure 13: The network performance in grid BS networks. (**a**) Network lifetimes; (**b**) Successful delivered packets from all STAs to the GW; (**c**) The average matching degrees.  
provision at BSs. Matching-EP copes with potential energy consumption differences in the long-term among BSs by employing topological position dependent energy assignment. In this study, we firstly take into account practical wireless features such as overhearing of packets, as well as the distribution of BSs and STAs in modelling the energy consumption and the energy provision difference among BSs. We propose basic models for energy consumption in WMI networks with line and grid topologies. These basic models can be used as estimates to for the implementation of Matching-EP in WMI networks. The numerical evaluation of Matching-EP for various topologies and network routing protocols is conducted by extensive packet-level simulations. The evaluation results show that Matching-EP enables high energy matching degrees at BSs, leading to 10%-40% improvement of network lifetime, as well as 5-40 improvement of user packet deliveries. In summary, matched energy provision can improve the performance of battery-powered networks, in terms of increasing network lifetime and volume of packets delivered. An approximate theoretical estimate of the data flow in a multi-hop wireless network can improve the effectiveness of matched energy provision. We envision that the proposed energy provision matching approach can be effectively applied to wireless networks powered with renewable energy power or wireless networks in emergency situations.  
Study design, modelling, analyses, simulation and writing: [PERSON], [PERSON], and [PERSON]; modelling and writing: [PERSON]; analyses, simulation and writing: [PERSON].  
The authors declare no conflict of interest.
# Multi-Source Neural Model for Machine Translation of Agglutinative Language
## 5 Results and Discussion
To further evaluate the effect of using different linguistic features, we separately incorporated the linguistic features of lemma, POS tag, and morphological tag into the proposed multi-source neural model for comparison. The experimental results for the Turkish-English machine translation task are shown in Table 7. From the table we can find that for the test datasets of tst2011, tst2012, and tst2013, incorporating the lemma feature into the multi-source neural model achieved the highest BLEU and ChrF3 scores while for the test dataset of tst2014, incorporating the morphological feature achieved the highest BLEU and ChrF3 scores. The results indicate that different linguistic features are appropriate for different datasets. Moreover, the combination of all the linguistic features achieves the best translation quality, which demonstrates that the proposed approach enables the NMT model to better utilize the source-side linguistic features and effectively integrate them together. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Neural Translation Model** & **BLEU Score** & **ChrF3 Score** \\ \hline NMT baseline [12] & 27.60 & 36.73 \\ single-source model [18] & 28.00 & 37.20 \\ multi-source model & 28.21 & 37.44 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The experimental results on Uyghur-Chinese machine translation task. Figure 4: The loss values as a function of the time steps in the different neural translation models for the Turkish–English machine translation task. Table 8 shows the translation samples of the NMT baseline model and multi-source neural model on Turkish-English machine translation. For the first translation sample, we can observe that the NMT baseline model misunderstands the subject in the source-side sentence and simply uses a pronoun to denote the Turkish word \"amerika'da\" (in America). Instead, the multi-source neural model captures the above information and generates an appropriate translation result. For the second translation sample, we can observe that the NMT baseline model makes a mistake on the meaning of the Turkish word \"cekiminden\" (from the shooting), which leads to an inaccurate translation result. Instead, the multi-source neural model understands the semantic information of the source sentence. The above translation examples indicate that the proposed model is more sensitive to the information of the subject, location, named entity, and the word class by utilizing the source-side linguistic knowledge.
# Effect of the Anti-Yaw Damper on Carbody Vertical Vibration and Ride Comfort of Railway Vehicle
###### Abstract  
The theoretical research on means to reduce the vertical vibrations and improve the ride comfort of the railway vehicle relies on a mechanical model obtained from the simplified representation of the vehicle, while considering the important factors and elements affecting the vibration behaviour of the carbody. One of these elements is the anti-yaw damper, mounted longitudinally, between the bogie and the vehicle carbody. The anti-yaw damper reduces the lateral vibrations and inhibits the yaw motion of the vehicle, a reason for which this element is not usually introduced in the vehicle model when studying the vertical vibrations. Nevertheless, due to the position of the clamping points of the anti-yaw damper onto the carbody and the bogie, the damping force is generated not only in the yawing direction but also in the vertical and longitudinal directions. These forces act upon the vehicle carbody, impacting its vertical vibration behaviour. The paper analyzes the effect of the anti-winding damper on the vertical vibrations of the railway vehicle carbody and the ride comfort, based on the results derived from the numerical simulations. They highlight the influence of the damping, stiffness and the damper mounting angle on the power spectral density of the carbody vertical acceleration and the ride comfort index.  
Keywords: railway vehicle; anti-yaw damper; vertical vibration; ride comfort +
Footnote †: journal: Applied Sciences