esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
[16] compared patients with normo-osmolarity and hyperosolarity on their preoperative evaluation and found that OSDI scores were significantly higher in the hyperosmolarity group 3 months after the surgery while there was no difference at 1 month after the surgery. They suggested that the presence of hyperosmolar tears before cataract surgery may be a risk factor for aggravating dry eye disease symptoms after surgery. Our subgroup analysis according to the preoperative tear osmolarity and MMP-9 results showed that there were no differences in tear osmolarity, MMP-9, or OSDI scores between the normal and abnormal groups. This suggests that preoperative osmolarity and MMP-9 results may not be predictive of the ocular surface conditions 1 month after the surgery. Moreover, patients with abnormal tear osmolarity and MMP-9 before surgery did not show an increased OSDI score or ocular discomfort subscale compared to normal preoperative patients with normal tear osmolarity and MMP-9. This may be due to the cutoff value of tear osmolarity to diagnose dry eye disease. There are several recommended cutoff values ranging from 304 mOsm/Lto 316 mOsm/L. [20; 21; 22; 23; 24; 25; 26]. The lack of consensus of tear osmolarity in diagnosing ocular surface disorder may have resulted in uncorrelation between tear osmolarity and OSDI score. However, ocular surface dysfunction can be asymptomatic and OSDI score alone may not reflect the existence of ocular surface disease. This is consistent with previous studies that patients with ocular surface dysfunction may not present symptoms of ocular discomfort [33; 57]. Ocular surface disease may cause abnormal tear film that could affect preoperative keratometric and topographic measurement, which could cause intraocular lens calculation errors and unsatisfactory visual results [58; 59; 60]. Therefore, even though preoperative tear osmolarity and MMP-9 measurements may not predict ocular surface conditions after FLACS, their role of screening ocular surface disease before surgery is valuable for satisfactory outcomes. Limitations of our study are its retrospective design and small sample size. This study did not have a control group that had not undergone FLACS. Additionally, the follow-up period was rather short to adequately analyze the long-term changes in tear osmolarity and MMP-9 after FLACS. Traditional examinations to evaluate dry eye disease, such as corneal fluorescein staining, tear film break-up time, and the Schirmer test, have not been conducted in this study. All surgeries were performed by a single surgeon, which may limit generalizability of the results. However, we believe that measuring tear osmolarity and MMP-9 using point-of-care diagnostics are objective tests and are sufficient to evaluate dry eye disease due to their high sensitivity and specificity. Despite these limitations, this is the first study to investigate the change in the MMP-9 level after cataract surgery and it provides valuable information about the ocular discomfort commonly seen after cataract surgery.
## 3 Performance Evaluation and Analysis  
In this section, the proposed WFCS algorithm has been evaluated by a large number of experiments performed on the simulated dataset and the real dataset. The real datasets include eight UCI benchmarking datasets [14] and a CFM56-type engine dataset (named as ENGINE (ENGINE data can be provided by sending email to the corresponding author)) with measurement noise which has been collected from Air Company. In order to obtain the simulated data, an aero-engine gas path data with Gauss noise (named as LTT) was obtained by a simulation software (developed by the Laboratory of Thermal Turbo machines at the National Technical University of Athens (Downloaded from [[http://www.ltt.mech.ntua.gr/index.php/softwarem/teachesmn](http://www.ltt.mech.ntua.gr/index.php/softwarem/teachesmn)]([http://www.ltt.mech.ntua.gr/index.php/softwarem/teachesmn](http://www.ltt.mech.ntua.gr/index.php/softwarem/teachesmn)))). The ENGINE and the LTT datasets present the aero-engine's states, including healthy states and degrade states. In these experiments, all datasets are normalized into (0, 1) [13].  
First, the datasets information, validation criteria and parameters setting are described. Then, the properties of the WFCS are investigated based on the experimental results of the Iris dataset. A detailed comparison with other three feature weighting fuzzy clustering algorithms (ESSC, WFCM, WSRFCM) and one un-weighted fuzzy clustering algorithm (FCS) is performed at last.
# Current Promising Strategies against Antibiotic-Resistant Bacterial Infections
## 4 Developing Alternatives to Antibiotics
### Probiotics  
Probiotic supplementation is accepted globally as a beneficial health strategy despite the lack of scientific evidence of its alleged effects. Current mechanisms that explain the protective effect of probiotics are limited to the regulation of the immune system, enhancement of the intestinal epithelial barrier, competition with pathogenic bacteria for nutrients, and bacteriocin-mediated interference [110]. The contribution of the microbiome to host defense against pathogen colonization and prevalence is called \"colonization resistance\" [111]. _Bacillus subtilis_, a common strain of human gut microbiota and component of probiotic formulae, can produce bactracin that interferes with the cell wall synthesis of MRSA and other GPBPs. However, in a randomized prospective study involving a 5-day treatment in healthcare workers, bactracin was inferior to muiprocin for suppressing nasal _S. aureus_[112]. Besides bactracin, _B. subtilis_ produces fengycins, which are a family of lipopeptides. Fengycins exhibited a strong ability to block the _S. aureus_ quorum-sensing system Agr, which is vital for _S. aureus_ gut colonization [113]. These studies suggest a strong potential for using alive _Bacillus_ strains as a probiotic therapy to prevent infections caused by _S. aureus_ colonization. Besides natural probiotics, engineered or synthetic probiotics are an additional promising option. For example, an engineered probiotic _E. coli_ Nissle 1917 equipped with quorum sensing and lysing devices sensed and killed 99% planktonic _P. aeruginosa_, and caused a 90% reduction in biofilm formation in an vitro system [114]. More importantly, this research group demonstrated that this engineered strain could be applied in animal models to suppress _P. aeruginosa_ infection [115]. Although probiotic therapies related to colonization resistance are clinically unavailable yet, the elucidation of mechanisms that are responsible for such resistance can pave the way to the successful development of probiotic treatments.
# Methods for Assessing Human-Machine Performance under Fuzzy Conditions
## 5 Triangular Fuzzy Numbers
### The Assessment Method Using TFNs  
In [36], we developed with the help of TFNs a method for assessing a system's mean performance, whose steps are the following:  
* Define the _mean value_ of a finite number of given TFNs A1, A2,..., n \(\geq\) 2, to be the TFN A = \(\frac{1}{n}\)(A1 + A2 +... + An).
* Assign a scale of numerical scores from 1 to 100 to the linguistic grades A = excellent, B = very good, C = good, D = fair and F = unsatisfactory as follows: A (85-100), B (75-84), C (60-74), D (50-59) and F (0-49).  
Figure 5: Graph and COG of the TFN (a, b, c).  
Use for simplicity the same letters to represent the above grades by the TFNs A = (85, 92.5, 100), B = (75, 79.5, 84), C (60, 67, 74), D (50, 54.5, 59) and F (0, 24.5, 49), respectively, where the middle entry of each of them is equal to the mean value of its other two entries.
* Assess the individual performance of the system components by the above five qualitative grades and assign one of the TFNs A, B, C, D, F to each of those components. Then, if n is the total number of the system's components and \(n_{X}\) denotes the number of the components corresponding to the TFN X, with X = A, B, C, D, F, the mean value M of all those TFNs is equal to the TFN \[\text{M }(a,\ b,\ c)=\frac{1}{n}\left(n_{A}\text{ A}+n_{B}\text{ B}+n_{C}\text{C}+n_{D}\text{D}+n_{F}\text{F}\right).\] (14)  
Using the TFN M (\(a,\ b,\ c\)) to evaluate the system's mean performance, it straightforward to check that its components a, b and c, respectively, are equal to \(\frac{85n_{A}+75n_{B}+60n_{C}+50n_{D}+0n_{F}}{n}\)\(\frac{925n_{A}+79.5n_{B}+67n_{C}+54.5n_{D}+24.5n_{F}}{n}\) and \(\frac{100n_{A}+84n_{B}+74n_{C}+59n_{D}+49n_{F}}{n}\).  
Then, Equation (13) gives that  
\[\text{X }(\text{M})=\frac{a+b+c}{3}\frac{92.5n_{A}+79.5n_{B}+67n_{C}+54.5n_{D}+24. 5n_{F}}{n}=\frac{a+c}{2}=b. \tag{15}\]  
Therefore, for the defuzzification of M (\(a,\ b,\ c\)), one needs to calculate only its middle component b. The value of X (M) provides a crisp representation of the TFN M evaluating the system's mean performance.  
An analogous assessment method was also developed in [37] using TpFNs instead of TFNs. Examples illustrating the above assessment method are presented in Section 6.3 to be compared with the use of grey numbers.
## 4 Discussion
### Antibacterial Activity  
Little is known about the direct antimicrobial potential of fructans. [PERSON] et al. [48] reported that fructan isolated from agave (_Agave tequilana_ Weber var. azul) demonstrated antimicrobial activity against _Salmonella typhimurium_. The minimum inhibitory concentration of fructans against _Salmonella_ under culture conditions was 3%, with the greatest effect observed at a concentration of 25%; thus, they have an antibacterial effect against this strain at 50% concentrations. It was reported that water-soluble polysaccharide dandelion showed an antibacterial effect against _E. coli_, _Bacillus subtilis_, and _St. aureus_ at concentrations of 100 mg/mL, with zones of 12.47 mm, 11.02 mm, and 15.26 mm, respectively [49]. Other authors have demonstrated the antibacterial activity of commercially available dandelion polysaccharides against _L. monocytogenes_, and their TEM analysis showed the potential of this polysaccharide to destroy cell membranes and the cell integrity of bacteria [50]. The agar diffusion method was also applied for the evaluation of the antibacterial potential of dandelion leaf polysaccharide at a concentration of 2.0 mg/mL. The authors reported inhibitory diameters against _E. coli_, _Salmonella_, _S. aureus_, and _L.monocytogenes_ of 9.4 \(\pm\) 0.2 mm, 11.6 \(\pm\) 0.6 mm, 11.8 \(\pm\) 0.7 mm, and 15.4 \(\pm\) 0.5 mm, respectively [51]. In our study, the tested dandelion inulin mainly inhibited the growth of Gram-positive bacteria, except for the growth of _St. aureus_ at a concentration of 1 mg/mL. Our findings suggest that dandelion and chicory inulin were inactive against the Gram-negative bacteria _E.coli_. This statement is consistent with previous report on chicory inulin (DP 2-60) which revealed that a concentration of 0.1% was inactive against _Escherichia coli_ ATCC 3398 and _Staphylococcus aureus_[52]. To our knowledge, the current study is the first to evaluate the antibacterial potential of dandelion inulin. The possible explanation for its activity could be due to its adsorption to the cell membrane, as was suggested in earlier studies [50; 51; 52]. It is well known that polysaccharides may cause the destruction of the inner cell membrane to increase permeability and accelerate electrolyte release. This action has led to bacterial cell death [51]. With these data, we suggest that dandelion inulin could be useful in the prevention of infections caused by _Listeria monocytogenes_ or _Salmonella_. However, to elucidate the mechanisms involved in this action, further future studies are needed.
## 2 Materials and Methods
### Nonlinear Least Squares Method to Minimize the Model Response Error  
A more conservative approach to the personalization of model (1) is the least squares minimization of the model simulation error with respect to the data in the form of sampled state responses. This approach considers a quadratic cost function to minimize the sum of the squared errors/residuals between the provided sampled states \(N(kT_{s}),\ L(kT_{s}),\ T(kT_{s})\) and the fitted model responses \(\hat{N}\big{(}kT_{s},\hat{\xi}\big{)},\ \hat{L}\big{(}kT_{s},\hat{\xi}\big{)},\ \hat{T}\big{(}kT_{s},\hat{\xi}\big{)}\) obtained by numerical solution of (1). The corresponding cost function has the form  
\[\begin{array}{c}I_{\text{LS}}\big{(}\hat{\xi}\big{)}=\sum_{k=1}^{n}[\lambda_ {N}\Big{(}\hat{N}\big{(}kT_{s},\hat{\xi}\big{)}-N(kT_{s},\hat{\xi})\Big{)}^{2}+ \lambda_{L}\Big{(}\hat{L}\big{(}kT_{s},\hat{\xi}\big{)}-L(kT_{s},\hat{\xi}) \Big{)}^{2}+\\ \lambda_{T}\Big{(}\hat{T}\big{(}kT_{s},\hat{\xi}\big{)}-T(kT_{s},\hat{\xi}) \Big{)}^{2}],\end{array} \tag{32}\]  
where \(\lambda_{N}>0,\lambda_{L}>0,\lambda_{T}>0\) are the weights.  
It is important to remark that since the model responses \(\hat{N}\big{(}kT_{s},\hat{\xi}\big{)},\ \hat{L}\big{(}kT_{s},\hat{\xi}\big{)},\ \hat{T}\big{(}kT_{s},\hat{\xi}\big{)}\) are not linear with respect to the estimated vector \(\hat{\xi}\), the linear regression cannot be used to find \(\hat{\xi}\) minimizing (32). This also means that the cost function given by (32) cannot be written as a quadratic form with respect to \(\hat{\xi}\) and minimized analytically, implying a nonlinear least squares problem. Therefore, the estimate \(\hat{\xi}\) must be determined by numerical optimization using some gradient-free algorithm such as the Nelder-Mead [57] or genetic algorithms [58]. It suggests that the main drawback of this conventional approach is the computational complexity of the personalization itself, which is significant due to the need for numerical optimization involving repetitive evaluation of cost function given by (32) and numerical solution of the model differential equations.  
This contrasts with the parametrization of the state responses and calculating the output of the MLP NN in the proposed approach. Therefore, it can be concluded that despite the significant burden involved in generating the training dataset and in the process of training itself, the proposed approach is superior in terms of the computational complexity of personalization.
# Impact of Cubic Symmetry on Optical Activity of Dielectric 8-srs Networks
## 1 Introduction
This leads to a breaking of cubic symmetry, which affects optical performance of fabricated structures [33]. Various methods have been developed to correct for asymmetry due to elongation of the focal spot, including apodization [35] and multi-line writing techniques [36; 37]. Whilst these methods can significantly reduce elongation, to remove asymmetry completely the most effective method is galvo-dithered direct laser writing (GD-DLW) [29; 30; 31; 33]. This fabrication method uses a galvo-mirror system to trace the focal spot in a circular motion within the focal plane. The circular motion exposes a larger lateral volume of material, whilst simultaneously reducing total exposure in the axial direction. Accordingly, asymmetry of the exposed volume can be reduced (Figure 2a). In the following we will describe our method for fabricating cubically symmetric 8-srs PhCs with an operative wavelength in the mid-IR using a custom-made GD-DLW system. Finally, we present and discuss results of numerical and experimental characterization of 8-srs PhCs. Figure 1: Formation of an 8-srs network. (**a**) Typical component of a right handed 1-srs network. (**b**) Three copies of the 1-srs network are translated by half a unit cell along [100], [010], and [110] crystallographic directions to form a 4-srs network. (**c**) A single copy of the 4-srs network is translated by 1/4 of a unit cell along the [111] crystallographic direction to obtain an 8-srs network.
# Adaptive Clutter Intelligent Suppression Method Based on Deep Reinforcement Learning
###### Abstract
It automatically adjusts the filter notch by adaptively estimating the clutter center frequency and spectral width to achieve the suppression of dynamic and static clutter, but the method requires that the clutter data for constructing the covariance matrix satisfy the independent and identically distributed (IID) condition. However, the actual clutter characteristics are complicated and variable by the environmental changes, which do not satisfy the IID conditions, resulting in the limited performance of traditional AMTI clutter suppression methods. In recent years, with the continuous development and application of deep learning, the application of intelligent methods to the radar field has achieved good application prospects and become a hot spot of current research. Article [22] introduced a deep convolutional neural network (CNN) to classify and identify the clutter amplitude distribution, obtaining sufficient clutter sample data obeying IID and improving the performance of the AMTI clutter suppression method. However, the experiment was only conducted in a specific clutter background situation, and its model generalization ability may be insufficient when facing different sea state and environmental changes. Article [23] utilized a CNN to train sea echo signals, constructed a clutter autocorrelation matrix by estimating parameters related to the sea clutter power spectrum, designed filter weight coefficients that match the clutter, and achieved the suppression of sea clutter. However, the method relies on the dataset and valid labels to train the CNN, and acquiring and labeling the radar echo data is very time-consuming and highly dependent on manual intervention. Given the limitations of traditional and intelligent clutter suppression methods, including poor adaptability in complex environments, limited generalization capabilities, and a heavy reliance on manual intervention, an adaptive clutter intelligent suppression method based on deep reinforcement learning (DRL) is proposed, which achieved online adaptive filter parameter adjustment of to-be-detected cell in complex environments for the first time. Traditional methods usually rely on fixed rules and a large number of manual interventions, which make it difficult to adapt to changing environments. Incontrast, an intelligent environment perception method of deep learning (DL) is introduced in this method, which enables each detected cell (agent) to have the ability to perceive and learn the complex environment online, achieve the feature extraction of environmental observation data, and automatically adjust the filter parameters through reinforcement learning (RL), significantly reducing the manual involvement. Through the innovatively designed reward and punishment mechanism and the introduction of a greedy strategy, agents interact with the environment to learn the optimal filter parameters, achieve effective suppression of clutter, increase the signal-to-clutter and noise rate (SCNR), and enhance target detection performance.
## 3 DBSCAN Algorithm
### Application of the DBSCAN Algorithm  
The ship trajectory based on AIS data includes ship latitude, longitude, SOG, and COG according to a time series. Therefore, the information contained in the AIS data was entered as a variable and applied to the DBSCAN algorithm. The application of the DBSCAN algorithm employs Scikit-learn [37] in Python. The result is shown in Figure 8.  
According to Figure 8, by applying the algorithm based on the arriving ship's trajectory, the turning section where the ship changed course was derived into three clusters and the berthing step was analyzed in the last step. At this time, the part listed between each cluster was designated as the leg. According to Figure 8, in applying the departure data, the unberthing stage and turning section corresponding to purple and blue, respectively, were the same as the analysis result pertaining to arrival. However, the area corresponding to the leg of the arrival was analyzed as an orange cluster. Therefore, the arriving ship's trajectory was classified into seven stages and divided into three turning sections, three legs, and one berthing stage. Additionally, in the case of departure, it was classified into six stages, divided into the one unberthing stage, two turning sections, and three legs. The classified stages were re-designated as four phases with the same properties, and the pattern was analyzed [38]. Figure 9 shows the framework for the pattern analysis of a ship's trajectory, with detailed items having been analyzed for the SOG, COG, and the ship's position.  
Figure 7: Reachability plot: (**a**) arrival ship trajectory data; (**b**) departure ship trajectory data.
# A Novel Inductor-Based Non-Dissipative Equalizer
## 5 Conclusions  
In this paper, a novel inductor-based non-dissipative equalizer (NIBNDE) is proposed. The structure of the NIBNDE, the equalization principle, theoretical analysis and performance comparisons with other equalizers are presented. The simulations show that, compared to the SSIE, the NIBNDE reduces the balancing time by 29.23% in four cells, 34.52% in six cells and 22.4% in sixteen cells, respectively. Compared to the PAE, the NIBNDE can greatly reduce the number of energy storage elements, especially when there are a large number of batteries in series. The experimental results verify the conclusion of the simulation and the equalization accuracy of the NIBNDE. In summary, the NIBNDE can reduce the number of energy storage elements while retaining a good equalization speed. Future research will focus on using the state of charge as an index of inconsistency to achieve higher precision of the proposed circuit.  
Conceptualization, C.L. and L.K.; Data curation, S.W.; Formal analysis, C.L. and L.K.; Funding acquisition, L.K.; Investigation, S.W.; Methodology, C.L. and L.K.; Project administration, L.K.; Resources, L.K.; Software, Z.W. and H.R.; Writing--original draft, C.L.; Writing--review and editing, S.W., Z.W. and H.R.  
This research was funded by Science and Technology Development Special Foundation of Guangdong, China (2017B010120001).  
Figure 12: Four cells’ voltage trajectories in simulation.  
Figure 13: The comparison of \(i_{L1}\). (**a**) pulse-width modulation (PWM) in the experiment. (**b**) PWM in the simulation. (**c**) \(i_{L1}\) in the experiment. (**d**) \(i_{L1}\) in the simulation.  
**Acknowledgments:** I would like to express my sincere gratitude to my supervisor, [PERSON], who has given me a lot of effective advice during the experiments and the paper-writing. I would also like to thank the anonymous reviewers for dedicating the time to review my paper despite their busy schedules.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 6 Conclusions and Future Work
### Future Work  
This work presents a prediction model to predict the cascade size of a source tweet with fake news. The cascade size of the source tweet can be treated as the influence or the impact of fake news. However, there is still room for improvement. First, we should consider features that provide rich information on news influence on social media, such as pictures from news articles, the geographical correlations between events and online users, etc. Second, when considering exogenous factors, using TF-IDF to find news keywords is challenging, as they require manual intervention. If the keywords do not map aptly to the news, the trends derived from Google Trends will be inaccurate. In future work, we plan to employ deep learning methods to accurately extract these keywords. Finally, exogenous sources need to be diversified, such as other microblogging platforms (e.g., Facebook and Instagram), video-sharing sites (e.g., YouTube), or news channels. Various kinds of platforms can be used alternately to explore which platform is paramount. Does the combination of different types of platforms result in better prediction performance? Or is it more beneficial to use only one kind of platform?