esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Micropolarizer Array Design
### Grid Groove Depth  
The groove depth is also an important parameter affecting the polarization performance of grating. The groove depth ratio is the groove depth ratio to the grating line width. As it is relatively difficult to maintain the morphology integrity of micro/nano structures with a sizeable depth-width proportion, and multiple etching or imprinting may easily damage the transfer pattern, researchers at home and abroad usually control the depth-width ratio to below 2:1 in the design and preparation of online grids. Therefore, this design should control the sum of the aluminium layer depth h\({}_{1}\) and zinc selenide layer depth h\({}_{2}\) to within 1 \(\upmu\)m.  
As shown in Figure 9, \(h_{1}\) is 0.2 \(\upmu\)m to 0.8 \(\upmu\)m, and T\({}_{\text{TM}}\) and the extinction ratio change with \(h_{2}\). It can be seen that no matter how much \(h_{1}\) is taken, T\({}_{\text{TM}}\) increases with \(h_{2}\), while the extinction ratio hardly changes with \(h_{2}\). The change of \(h_{1}\) has little influence on T\({}_{\text{TM}}\)  
Figure 8: Effect of duty ratio on polarization performance of grating.  
Figure 7: Effect of period on polarization performance of the grating. (**a**) Influence on TM wave transmittance, (**b**) influence on extinction ratio.  
The T\({}_{\text{TM}}\) levels at the four times are similar, but the influence on the extinction ratio is very obvious. The larger the value of \(h_{1}\) is, the greater the extinction ratio is. When \(h_{1}\) is 0.8, the extinction ratio is significantly higher than the other three times. In conclusion, in order to meet the requirements of a large extinction ratio and obtain high transmittance, the value of \(h_{1}\) should be between 0.6 and 0.8, and \(h_{2}\) should be as large as possible in line with the process level.  
As shown in Figure 10, the linear chart of TM wave transmissivity under three groups of values is shown. When \(h_{1}=0.6\) and \(h_{2}=0.4\), the transmittance can reach more than 80%, and the extinction ratio can reach about 45. Finally, the values of each parameter of the designed grating are placed in Table 3.  
Figure 10: TM linear plot for three sets of values.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Substrate Material** & **ZnSe** \\ \hline Grid material & Al \\ Grating period (\(\Lambda/\mu\)m) & 1 \(\mu\)m \\ Duty cycle (f) & 0.5 \\ Groove depth (\(h_{1}\),\(h_{2}\), \(\mu\)m) & 0.6; 0.4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Design index of the grating.  
Figure 9: Influence of different groove depths on polarization performance of the grating.
_Antibiotics_**2014**, \(3\), 509-526; doi:10.3390/antibiotics3040509  
_artibiotics_  
**ISSN 2079-6382**  
www.mdpi.com/journal/antibiotics  
Anti-Biofilm and Immunomodulatory Activities of Peptides That Inhibit Biofilms Formed by Pathogens Isolated from Cystic Fibrosis Patients  
**[PERSON] 1, [PERSON] 1, [PERSON] 2, [PERSON] 1, [PERSON] 1, [PERSON] 1, [PERSON] 1, [PERSON] 3, [PERSON] 4, [PERSON] 2, [PERSON] 2 and [PERSON] 1,*  
1 Centre for Microbial Diseases and Immunity Research, Department of Microbiology and Immunology, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada; E-Mails:  
cesar(@)hancocklab.com (C.D.L.F.-N.); sarah(@)hancocklab.com (S.C.M.);  
[EMAIL_ADDRESS] (L.J.); [EMAIL_ADDRESS] (E.B.M.B.);  
[EMAIL_ADDRESS] (M.E.); [PERSON](F.R.)  
2 Division of Endodontics, Department of Oral Biological and Medical Sciences,  
Faculty of Dentistry, University of British Columbia, Vancouver, BC, V6T 1Z3, Canada;  
E-Mails: [EMAIL_ADDRESS] (Z.W.); [EMAIL_ADDRESS] (Y.S.);  
[EMAIL_ADDRESS] (M.H.)  
3 Department of Pediatrics, University of British Columbia, Vancouver, BC, V6H 3V4, Canada;  
E-Mail: [EMAIL_ADDRESS]  
4 Athabasca University, Athabasca, AB, T9S 3A3, Canada; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: bob(@)hancocklab.com;  
Tel: +1-604-822-2682; Fax: +1-604-827-5566.  
External Editor: Dr. [PERSON]  
_Received: 18 August 2014; in revised form: 8 October 2014 / Accepted: 14 October 2014 / Published: 20 October 2014_  
**Abstract:** Cystic fibrosis (CF) patients often acquire chronic respiratory tract infections due to _Pseudomonas aeruginosa_ and _Burkholderia cepacia_ complex (Bcc) species. In the CF lung, these bacteria grow as multicellular aggregates termed biofilms. Biofilms demonstrate increased (adaptive) resistance to conventional antibiotics, and there are currently no available biofilm-specific therapies. Using plastic adherent, hydroxyapatite and flow cell biofilm models coupled with confocal and scanning electron microscopy, it was demonstrated that an anti-biofilm peptide 1018 prevented biofilm formation, eradicatedmature biofilms and killed biofilms formed by a wide range of _P. aeruginosa_ and _B. cenocepacia_ clinical isolates. New peptide derivatives were designed that, compared to their parent peptide 1018, showed similar or decreased anti-biofilm activity against _P. aeruginosa_ biofilms, but increased activity against biofilms formed by the Gram-positive bacterium methicillin resistant _Staphylococcus aureus_. In addition, some of these new peptide derivatives retained the immunomodulatory activity of 1018 since they induced the production of the chemokine monocyte chemotactic protein-1 (MCP-1) and suppressed lipopolysaccharide-mediated tumor necrosis factor-\(\alpha\) (TNF-\(\alpha\)) production by human peripheral blood mononuclear cells (PBMC) and were non-toxic towards these cells. Peptide 1018 and its derivatives provide promising leads for the treatment of chronic biofilm infections and hyperinflammatory lung disease in CF patients.  
Keywords:anti-biofilm; immunomododulation; peptides; antibiotic-resistance; cystic fibrosis
# Wavelet Time-Frequency Analysis on Bridge Resonance in Train-Track-Bridge Interactive System
## 3 Time-Frequency Interaction Analysis on the Train-Track-Bridge System
### Evolutionary Reaction Analysis from the Bridge on the Track and the Train Subsystem  
In order to study the time-frequency evolutionary characteristics on the internal reaction from the bridge subsystem on the track and the train, the speed conditions of 350 and 585 km/h were selected, respectively, corresponding to the nonresonant and resonant bridge conditions, and the corresponding first-order loading frequencies are 3.89 and 6.50 Hz, respectively. The CWT scalograms of the first car body, one of the mid-sleepers, and mid-rail vertical acceleration response are shown in Figure 10:  
Figure 10: Scalogram for the track and the car body vertical vibratory resonant acceleration response: (**a**) First car body response scalogram under nonresonant bridge; (**b**) First car body response scalogram under resonant bridge; (**c**) Mid–rail response scalogram under nonresonant bridge; (**d**) Mid–rail response scalogram under resonant bridge; (**e**) Mid–sleeper response scalogram under nonresonant bridge; (**f**) Mid–sleeper response scalogram under resonant bridge.  
By comparison, between Figure 10a,b, the frequency band for the car body response is extremely scattered and there is no significant and stable time-frequency band which can clearly present the stable bridge natural frequency (6.5 Hz). In contrast, it can only be seen that the scalogram value increases with the increase in the train speed which is positively correlated to the velocity and the acceleration of track irregularity. Therefore, the car body response is dominated by the track irregularity excitation which has masking effect on the bridge response.
2. By comparison between Figure 10c,d, the high frequency band for the mid-rail presents the multiple peaks fluctuating phenomenon, which signifies the periodic loading process conducted by the continuous movement of multiple vehicle elements. Simultaneously, with the increase in train speed, the former high frequency band will move upwards to a higher frequency band. In terms of the low frequency band within the sub-windows, both of them contain the components of bridge natural frequency, which signifies that the feedback comes from the bridge response and in particular, will be intensified under the bridge resonant condition.
3. Figure 10e,f share the similar time-frequency characteristics to (c) and (d). The feedback which comes from the bridge response will be intensified under the bridge resonant condition. Particularly, the sleepers are closer to the bridge than the rail; therefore, the feedback on the sleepers is more intense than that on the rail.
4. In general, within the low-frequency band, the resonant bridge will have a more significant impact on the track subsystem, especially for the substructure closer to the bridge deck, than the nonresonant one, while the impacts from the bridges almost neglect the car body.
## 5 Discussion
### Flood Signal Detection  
Following [21], a single global and conservative threshold was used for flood signal detection with microwave data. The influence of the local incidence angle on backscattering hinders the use of a single threshold for the entire scene to a certain extent, but the range of the incident angle of Sentinel-1 is moderate. If the threshold is set higher (lower), the omission error and true positive rates are improved (worsened). Future research will set thresholds as functions of the incidence angle (e.g., piecewise or linear functions) or perform empirical regression-based normalization of the backscatter coefficient to a reference incidence angle [52] by considering the influence of the incidence angle.  
The weights of SAR and optical observation in our study are identical, while they could be adjusted depending on terrain, weather, and other conditions. The weight of SAR observation could be reduced when applied to mountainous regions because of the shortages resulted from its acquisition mechanism, such as fore-shortening, overlaying, and shadowing. The sensitivity of backscatter coefficient to soil moisture and ground surface roughness should also be considered.
# Computer Modelling of Heliostat Fields by Ray-Tracing Techniques: Simulating the Sun
## 3 Sun Radiation  
The sun rays received at Earth are affected by three issues: (1) the rays are non-parallel; (2) the distance Earth-Sun changes during the year, affecting the linearity; and (3) the radiation intensity decreases rapidly from the center to the border of the Sun disk. These features can be mathematically simulated, as shown below. For many analyses, these effects might be ignored, as they barely affect the quantitative thermal output produced at the target.
# Correlation Mapping of Perfusion Patterns in Cutaneous Tissue
## 3 Results
All  
Figure 5: (**a**) Frame from the video record with marked points of signal extraction; (**b**) PPGI signals between 200 s and 400 s from different foot locations after tracking (colours of PPGI signals correspond to marked points on foot) together with signals obtained using the BIOPAC device (ECG, RR, PPG). The THD section is highlighted on the graph with black dashed horizontal lines; (**c**) zoom-in area from 330 s to 350 s of the recording (ECG, PPG, and 3 PPGI signals). values of \(\hat{R}_{xy,\text{norm}}\) are normalized according to the stage before DBT was performed. In DM patients (subjects 1, 2), there is a noticeable decrease in perfusion during DBT and a subsequent increase after DBT compared to pre-test values. In healthy subjects (3, 4), the pattern is slightly different. Although there was a decrease in tissue perfusion during DBT, the post-test perfusion value did not reach the pre-test level, except in subject 3 in the location of lower ROI. Figure 6: Correlation maps of DM (**1**,**2**) and healthy (**3**,**4**) subjects (one subject in a row). The first column shows a frame from the recording with a bounded region of interest (white rectangle). The coloured maps represent the output from the correlation mapping script before, during, and after the DBT, respectively. In the right part, there are the signals from the places marked on the first column (the colours of the signals correspond to the marked points). The black signal is the PPG obtained with the BIOPAC device from the little toe of the proband’s foot.
## 9 Performance Analysis and Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**MS (\(\mathbf{u_{0}}\))**} & **SX (\(\mathbf{u_{i}}\)\(\
eq\)0)** \\ \hline \multicolumn{2}{c}{User keys} & \(k_{0}\),\(k_{1}\), \(k_{2}\),..., \(k_{n}\) & \(k_{0}\),\(k_{i}\) \\ \hline \multicolumn{2}{c}{Group keys} & \(gk_{1}\), \(gk_{2}\),..., \(gk_{m}\) & \(gk_{1}\), \(gk_{2}\),..., \(gk_{m}\) \\ \hline \multirow{3}{*}{counters} & Unicast & \(Count_{1}\), \(Count_{2}\),... \(Count_{n}\) & \(Count_{i}\) \\ \cline{2-4}  & Broadcast & \(Count_{0}\) & \(Count_{0}\) \\ \cline{2-4}  & Multicast & \(GCount_{1}\), \(GCount_{2}\),... \(GCount_{m}\) & \(GCount_{x}\), \(GCount_{y}\),... \((x,y\in[1,m])\) \\ \hline \multirow{3}{*}{Additional values} & Unicast & \(C_{1}\), \(C_{2}\),..., \(C_{n}\) & \(C_{i}\) \\ \cline{2-4}  & Broadcast & \(C_{0}\) & \(C_{0}\) \\ \cline{2-4}  & Multicast & \(GC_{1}\), \(GC_{2}\),..., \(GC_{m}\) & \(GC_{x}\), \(GC_{y}\),... \((x,y\in[1,m])\) \\ \hline \multirow{3}{*}{Session key} & Unicast & \(sk_{1}\), \(sk_{2}\),..., \(sk_{n}\) & \(sk_{i}\) \\ \cline{2-4}  & Broadcast & \(sk_{0}\) & \(sk_{0}\) \\ \cline{1-1} \cline{2-4}  & Multicast & \(gsk_{1}\), \(gsk_{2}\),..., \(gsk_{m}\) & \(gsk_{x}\), \(gsk_{y}\),... \((x,y\in[1,m])\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The information that needs to be saved in MS and SXs. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Communication Ends** & **MS (\(\mathbf{u_{0}}\))** & **SX (\(\mathbf{u_{i}}\)\(\
eq\) 0)** \\ \hline \multirow{3}{*}{_counters_} & Number of keys & \(<2n+2m+2\) & \(4+m\), \(4+2m\) \\ \cline{2-4}  & Number of Counters & \(n+m+1\) & \(2,2+m\) \\ \cline{2-4}  & Number of Additional values & \(n+m+1\) & \(2,2+m\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The information that needs to be saved in MS and SXs. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Storage Cost (Kbytes)** \\  & **(SXs = 1000 to 10,000)** \\ \hline \multirow{3}{*}{Number of DR project} & \(m=5\) & 0.448 \\ \cline{2-4}  & \(m=10\) & 0.768 \\ \cline{1-1} \cline{2-4}  & \(m=15\) & 1.088 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Storage cost according to number of DR project and SXs (1000–10,000).
## 5 Results of QoE\({}_{i}\) Modeling and Discussion
### QoE\({}_{i}\) Probability Model  
Table 3 shows the results of the _distribution fitting_ procedure, where the results of the Anderson-Darling (AD) statistic test are given, as well as associated \(p\)-values, and the distributions are ranked according to AIC. It is obvious that the LogNormal function has the lowest value, AIC = 26.61, and therefore it describes best the probability density distribution of the variable \(QoE_{i}\). So, although a smaller value of the AD test indicates a better fit of the data with the observed type of distribution (logistic distribution has the smallest value of AD = 9.604), the choice was made according to AIC. Smaller \(p\)-values represent evidence against the null hypothesis of a statistical fit of the data with a particular distribution.  
Figure 3 graphically shows the shape of the LogNormal probability distribution density function.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Distribution** & **AD** & \(\boldsymbol{p}\) & **AIC** \\ \hline LogNormal—three parameter & 13.47 & 0.000 & 26.61 \\ LogLogistic—three parameter & 12.16 & \textless{}0.005 & 47.09 \\ Exponential—two parameter & 37.41 & \textless{}0.001 & 102.3 \\ Logistic & 9.604 & \textless{}0.005 & 292.0 \\ Normal & 10.53 & 0.000 & 295.5 \\ Smallest extreme value & \textless{}157.0 & \textgreater{}0.250 & 176443 \\ Largest extreme value & \textless{}86.76 & \textgreater{}0.250 & 1,579,994,965 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of fitting different tested distributions with a \(QoE_{i}\) variable.  
Figure 3: LogNormal probability distribution density function of the \(QoE_{i}\) variable.
# Exploring Hybrid-Multimodal Routing to Improve User Experience in Urban Trips
## 3 Evaluation Framework
### Overview  
Figure 1 illustrates the process of evaluation of urban trips. We start with a dataset containing origins and destinations of urban trips. After, we use our proposed mobility flow clustering technique, as described in Section 3.3, to create a weighted graph of the urban flows. This step makes feasible the evaluation of huge datasets by applying the model on the most relevant flows. Once the graph is obtained, we evaluate possible driving ways for a given flow and combine these driving ways with traffic data to identify congested segments in the trip. These segments then are replaced with alternative transportation modes in the route combination phase. After all traditional and hybrid route options are obtained, we assess the gains/losses in terms of duration and cost, and also we use our proposed user experience model (as described in Section 4) to evaluate the quality of all options.
## 4 Generic PSO Algorithms in Haskell
In this case, pso' is executed while the number of remaining iterations is not zero, calling the auxiliary function oneStep in each iteration. When the iterations are finished, function obtainBest returns the best solution found so far. Let us now concentrate on how each step is performed. First, each particle is updated. This is done by function updateParticle, that applies the equations described in Section 3 to update the velocity and position. Moreover, this function also checks if the fitness of the new position is better than the best solution found so far. Thus, the output of the function is a list of pairs (Bool, Particle) where the boolean value is True if the new position of the particle is better than the best position found globally so far. Once all particles have been updated, we check if any of them have improved the best solution or not. If some particles have improved the best solution, we sort the new best solutions and select the new best one. Finally, all particles are updated with the information about the new best global position. The next code shows how each step is done:oneStep :: [(Double,Double)] -> WPGParams -> (Position -> Double)  -> Boundaries -> [Particle] -> [Particle]  oneStep rs wpg f bo pas  |null newBests = newPas  |otherwise = map(updateGlobalBest newBvBp)newPas  where newBsPas = zipWith (updateParticle wpg f bo) rs pas  newPas  = map snd newBSPas  newBests = map snd (filter fst newBsPas)  newBvBp = obtainBest [minimum newBests]  
updateParticle :: WPGParams ->(Position->Double)->Boundaries  ->(Double,Double)->Particle->(Bool,Particle)  updateParticle (w,p,g) f bo (rp,rg) (b1v,b9v,po,s,blp,bgp)  |newFit < bgv = (True,(newFit,newFit,newPos,newVel,newPos,newPos))  |newFit < blv = (False,(newFit,b9v,newPos,newVel,newPos,bgp))  |otherwise = (False,(blv,b9v,newPos,newVel,blp,bgp))  where newVel = w*s + g*rp*&(blp-&po) + g*rg*&(bgp-&po)  newPos = limitRange bo (po + g*newVel)  newFit = f newPos  
where +& and -& perform the sum and difference on arbitrary large vectors, while *& multiplies all the elements of a vector by a single real number. Note that the higher-order nature of Haskell makes it simple to define a generic function dealing with PSO algorithms, as the fitness function can be passed as input parameter to the main pso function. Moreover, it is also easy to define fitness functions over arbitrary large dimensions. For instance, let us consider the following function that will be used in the experiments shown in this paper:  
\[f(x)=\sum_{i=1}^{n}-x_{i}sin(\sqrt{|x_{i}|})\]  
This function was considered in [39] as a function which is specially hard to optimize, because the number of local minima increases exponentially with the number of dimensions of the searching space. Figure 1: Basic flowchart of function pso. In particular authors used \(30\) dimensions, where all dimensions are bound within the interval \([-500,500]\). We can trivially codify this function in Haskell as follows:  
fit :: Position -> Double  fit xs = sum (map fit' xs) where fit' xi = -xi * sin (sqrt (abs xi))  bo :: Boundaries  bo = replicate 30 (-500,500)
## 3 Effects of Joints on the Tensile Properties
### Effects of Intersecting Joints on the Tensile Properties
#### 3.3.3 Effect of Joint Density of Intersection Joints  
In this study, the effects of joint intensity on \(\sigma_{t}\) and \(E_{t}\) are investigated by means of the number of joints (\(n\)) inside the specimen. Four sets of models with different dip angles (\(\beta\) = 40, 60, 80, and 100\({}^{\circ}\)) were built (as illustrated in Figure 14) under the condition of \(d\) = 5 mm, and for each set of the model, six subsets with different numbers of joints (\(n\) = 4, 6, 8, 10, and 12) were analyzed. The test program specifics are shown in Table 11 and the stress-strain curves under uniaxial tensile load are shown in Figure 15. The results are shown in Table 12 Figure 15: Tensile behaviors of intersection-jointed rock specimens with different numbers of intersection joints.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Sets of Models** & \(\beta\) & **Subsets with Respect to \(n\)** \\ \hline \multirow{4}{*}{\(1\)} & & \(n=4\) \\  & & \(n=6\) \\  & \(1\) & \(40^{\circ}\) & \(n=8\) \\  & & \(n=10\) \\  & & \(n=12\) \\ \hline \multirow{4}{*}{\(2\)} & & \(n=4\) \\  & & \(n=6\) \\  & \(2\) & \(60^{\circ}\) & \(n=8\) \\  & & \(n=10\) \\  & & \(n=12\) \\ \hline \multirow{4}{*}{\(3\)} & & \(n=4\) \\  & & \(n=6\) \\  & \(3\) & \(80^{\circ}\) & \(n=8\) \\  & & \(n=10\) \\  & & \(n=12\) \\ \hline \multirow{4}{*}{\(4\)} & & \(n=4\) \\  & & \(n=6\) \\  & \(n=8\) \\  & & \(n=10\) \\  & & \(n=12\) \\ \hline \end{tabular}
\end{table}
Table 11: Model designs for rock specimens with two parallel joints.  
As can be seen, for each subset with the same \(\alpha\), the peaks of the curves are almost identical while the slopes are notably effected by \(n\). Taking the model set with \(\beta=80^{\circ}\) as an example, as listed in Figure 15, when \(n\) increases from 2 to 12, \(E_{t}\) significantly drops from 0.73 to 0.34 GPa, while \(\sigma_{t}\) remains constant. The influencing pattern of \(n\) on \(E_{t}\) is identical for different values of \(\alpha\). These results indicate that the fracture intensity of a rock mass has a great effect on \(E_{t}\) but a negligible effect on \(\sigma_{t}\).  
The relationship between \(n\) and \(E_{t}\) with different values of \(\beta\) after fitting is shown in Figure 16. As can be seen, \(E_{t}\) is negatively correlated to \(n\), and the relationship varies with different values of \(\beta\). The change in \(E_{t}\) with \(n\) will be less significant in case of a higher value of \(\beta\).