esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Traverse Stage and Model Validation
#### 4.2.1 Temperature Distribution and Plastic Strain
Figure 13: Heat affected zone’s (HAZ) thermal cycle for the FSW welded joint at 550 rpm. martensite and Cr-rich M\({}_{23}\)C\({}_{6}\) carbides. The number of M\({}_{23}\)C\({}_{6}\) carbides increased with an increase in the tempering temperature [49]. It is clear from these results and data that HAZI reaches the intermediate temperature between A\({}_{1}\) and A\({}_{3}\), while HAZII and III are tempered at high temperature and low temperature, respectively. Moreover, the HAZ width in both experimental and simulation work increased with increasing the tool rotation. The same result was reported by [PERSON] et al. [50]. The increase in the tool rotation softens the material because of the high heat generated and bolsters the extending of the heat removal across the workpiece. Figure 7b,c show that the SZ has a basin-like shape that is symmetric across the weld centerline in both experimental and computed results. However, with an increase in the tool rotations (Figure 8b,c, Figure 9b,c, Figure 10b,c), the SZ at the RS takes a wavy shape. This may be attributed to the higher shear stress at the AS compared to the RS. At the highest tool rotation, the sample has a more complex material flow. In addition, the equivalent plastic strain increases with an increase in the tool rotation. Increasing the tool rotations enhances the tangential velocity around the pin, leading to a higher deformation rate, or in other words, a higher strain rate. Compared to the AS, the plastic strain at the tool shoulder edge in the RS is high. This may be attributed to the excessive flash formed at the RS. It is evident that there are no volumetric defects in the cross section of the welds, except the band structure shown in Figures 9b and 10b. The band structure at the bottom AS shown in Figures 9b and 10b, specified by black arrows, may be due to the tool pin tip wear at high tool rotation rates. In addition to the tool wear, the peak temperature at the SZ of the sample welded at 550 rpm is higher than 1300 \({}^{\circ}\)C. Below this temperature, the delta ferrite phase forms, according to [PERSON] et al. [51]. The delta ferrite does not transform to austenite and then martensite during the cooling process because of the high cooling rate. Therefore, it is expected that the clear band structure at the top AS, specified by the red arrow in Figure 10b, is due to the tool wear and the delta ferrite phase formed at high temperature. Good agreement between the computed results and the experimental results indicates that the model can be utilized to estimate the thermal cycles and weld macrostructure at different welding conditions.
# Nano-HPLC-HRMS Analysis to Evaluate Leptin Level in Milk Samples: A Pilot Study
## 5 Conclusions  
The developed analytical method based on immunoaffinity purification and analysis with nano-HPLC-HRMS instrumentation of milk samples shows a good sensitivity and selectivity suitable to identify and quantify leptin in human breast and bovine milk and in infant formula products. For this purpose, we selected two typical peptides, one as qualifier and one other as quantifier, derived from tryptic digestion of the human protein. Peptides were characterized and a deep study of their tandem mass fragmentation pathway was led.  
Commercially available bovine milk samples were used as quality control samples and, after leptin fortification, as a matrix for calibration curve development.  
After the immunoaffinity process of purification, obtained using specific and selective antibodies anti-leptin, samples were analyzed with nano-HPLC-HRMS developed method. In human breast milk samples leptin were detected and quantified (average of 6.70 ng mL\({}^{-1}\)); bovine leptin was identified by Mascot software in bovine colostrum. No human, nor bovine leptin (searching for the specific peptides formed by tryptic digestion) was quantified both in bovine milk and in infant formula products (under obtained LLOQ for human leptin). This finding confirmed the high probability to loss leptin during milk processing, because of its lipophilicity.  
Last but not least, the developed immunoaffinity/nano-HPLC-HRMS method used a reduced amount of organic solvents (approximately 10 \(\upmu\)L for each analysis) and avoid the use of radioactive assays.  
Conceptualization, F.D.B., F.S. and C.M.; methodology, F.D.B. and E.M.; software, D.G.; formal analysis, F.D.B. and D.G.; data curation, F.D.B.; writing--original draft preparation, F.D.B.; writing--review and editing, C.M. and F.S.; supervision, C.M.; project administration, C.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
# Remote Speckle-Based Measurements of Backward Brillouin Acoustic Vibrations in Optical Fibers
## 3 Materials and Methods  
### The Method of Measurements  
The maximal vibration frequency that can be detected by the technique presented above depends on the frame rate of the camera that is used for recording the speckles pattern as a function of time. As we mentioned above, the typical value for Brillouin frequency is about 10 GHz, which is much higher compared to the maximal camera's frame rate. Thus, to detect the backward Brillouin effect by using speckles analysis, we need to find a way to reduce the frequency of the acoustic vibrations. This can be achieved by performing amplitude modulation to the Pump wave. If we modulate the pump with frequency modulation of \(\omega_{m}\) we will get two sidebands to the main laser frequency \(\omega_{p,0}\)[31]:  
\[\omega_{p1}=\omega_{p,0}-\omega_{m} \tag{3}\]  
\[\omega_{p2}=\omega_{p,0}+\omega_{m} \tag{4}\]  
According to Equation (2), for each pump we will get the corresponding acoustic wave, where the frequencies of those waves are given by:  
\[\Omega_{B,i}=\frac{2\
u_{a}}{c/n}\omega_{p,i} \tag{5}\]  
where \(i=0,1,2\). For those pump waves we also get the corresponding back-reflected Stokes waves, with frequencies of:  
\[\omega_{s,i}=\omega_{p,i}-\Omega_{B,i} \tag{6}\]  
\(B\), we also have the cross interaction between each pump wave and the generated Stokes waves of the other pump waves [32]. For example, the cross interaction between pump number 1 with frequency \(\omega_{p,1}\) to Stokes number 2 \(\omega_{s,2}\) will generate the next acoustic frequency:  
\[\Omega_{c,1}=\omega_{p,1}-\omega_{s,2}=\omega_{p,1}-(\omega_{p,2}-\Omega_{B, 2}) \tag{7}\]  
And by using the Equation (3) we get:  
\[\Omega_{c,1}=\Omega_{B,2}-2\cdot\omega_{m} \tag{8}\]  
And similarly, it can be shown that the cross interaction between Pump number 2 to Stokes number 1 will generate the next acoustic frequency:  
\[\Omega_{c,2}=\Omega_{B,1}+2\cdot\omega_{m} \tag{9}\]It can be seen from Equation (8) that if we choose the modulation frequency to be very close to the Brillouin frequency \(\Omega_{B,2}\) we can get acoustic vibrations with a frequency that may be detected by using our relatively fast (but not 10 GHz fast) camera.
## 3 Results and Discussion
### Mispe Optimization  
The performance of cartridges packed with MIP particles or NIP particles as sorbents for SLX SPE was investigated. Solid phase extraction (SPE) conditions, such as solution loading, washing, and elution, are carefully optimized for high extraction efficiency. This  
Figure 4: Fitting the adsorption of MIP and NIP to isotherm adsorption model, [PERSON] (**a**) and [PERSON] (**b**).  
study tested isopropanol and water to find a suitable loading solvent. Specifically, 1 mL of SLX solution, prepared in the above solvent at a concentration of 2 g/mL, was introduced into the SPE cartridge using 1.0 mL/min flow rate f. Figure 4 shows that only 2.63 \(\pm\) 0.19% (isopropanol) and 3.11 \(\pm\) 0.23% (water) template were released into the MIP cartridge (MIP3), whereas 70.53 \(\pm\) 1.96% (isopropanol) and 54.86 \(\pm\) 1.16% (water) template were released into the NIP cartridge (NIP3). Therefore, isopropanol and water were chosen as loading solvents.  
In the sample loading process, the template in the sample may be specifically or non-specifically absorbed by the polymer [55]. Therefore, a suitable washing solvent is required to remove the non-specifically bonded mold, while maintaining the specifically bonded mold. Therefore, the solvent used is required to remove the template from the NIP, while maintaining the template in the MIP [56]. In this study, acetonitrile was investigated to optimize washing conditions. The results showed that this effect was obtained by using acetonitrile at a flow rate of 1 mL/min. Figure 5 shows that 2.80 \(\pm\) 0.42% of SLX was released from MIP3 and 16.09 \(\pm\) 1.88% from NIP3. The smaller amount of SLX released than MIP indicates that acetonitrile is a suitable washing solvent.  
Figure 5: Optimization of MIP and NIP: (**a**) loading with isopropanol, (**b**) loading with water.  
Considering the solubility of SLX and the hydrogen bonding interactions between the template and MIP, a methanol:acetic acid (99:1) mixture was tested to find the optimal elution solution. It was found that the elution of SLX was obtained using 4 \(\times\) 1 mL of methanol:acetic acid (99:1, \(v/v\)) at a flow rate of 0.5 mL/min. Figure 4 shows that 99.04 \(\pm\) 0.90% of SLX could be extracted from MIP3 and 13.04 \(\pm\) 1.59% from NIP3. The higher recovery from the MIP compared to the NIP showed that the elution solvent was suitable.
# A Dual-Band Modified Franklin mm-Wave Antenna for 5G Wireless Applications
## 1 Introduction
The main advantage of the antenna is that the aforementioned modifications are introduced into the radiating part and ground plane in such a manner that there is no additional increase in the area of the antenna, i.e., the antenna remains compact.
# Clustering on the Chicago Array of Things: Spotting Anomalies in the Internet of Things Records
## 3 Methodology  
We aim to study the usefulness of clustering approaches on the Chicago AoT to determine its usefulness for anomaly detection. Clustering was chosen since the Chicago AoT is not labeled. The goal is to build a mechanism by which we can auto-label the dataset. We focus on sensor-level clustering in this work. For this work, we are focusing on three available clustering methods:  
* K-Means: The baseline clustering algorithm.
* DBSCAN: A modern clustering algorithm.
* Hierarchical DBSCAN: A modification of DBSCAN that utilizes tree hierarchies in its clustering. Each clustering tool was chosen for its popularity and usefulness.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Citation** & **Goal** & **Dataset** & **Approaches** & **Notes** \\ \hline \multirow{2}{*}{[24]} & unusual human & \multirow{2}{*}{phone position} & BDT regression, Seasonal & \multirow{2}{*}{uses last-value imputation} \\  & movement detection & & Trend decomposition & \\ \hline \multirow{2}{*}{[25]} & fault detection & T2T manufacturing & GDD, TZD,SVM & uses mean imputation \\ \hline \multirow{2}{*}{[13]} & personal health and & \multirow{2}{*}{CHART, Chicago AoT} & gridding, correlation, & focuses on correlation, not \\  & environmental factors & & logistic regression & anomaly detection \\ \hline \multirow{2}{*}{[12]} & \multirow{2}{*}{AD in sensors} & \multirow{2}{*}{Airbox} & TSAD,Tukey’s test and & \multirow{2}{*}{utilized  heuristics/metaheuristics} \\  & & & reasonable & \\  & & measurement drift & & for AD \\ \hline \multirow{2}{*}{[14]} & prediction of urban & NOAA (ground truth), & \multirow{2}{*}{tensor factorization} & removing of outliers (not \\  & heat islands & Chicago AoT & & detection explicitly), \\  & & & imputation was used \\ \hline \multirow{2}{*}{[26]} & prediction of urban & \multirow{2}{*}{Satellite Data, Chicago AoT} & AD using quantiles, & \multirow{2}{*}{rules-based AD, removal of  outliers for prediction,  rather than detection} \\  & heat islands & & prediction using RFR, MLP, & \\  & & SVM and & & \\  & & polynomial regression & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of approaches reviewed.
## 3 Results and Discussions
### Far-UV CD Spectroscopy  
Far-UV CD spectra are strongly dependent on the secondary structure of peptides [43; 44]. CD spectra of L-Phes and D-Phes in DMPC/DHPC bicelle (DMPC:DHPC=1:1) are shown in Figure 1. Bicelle is used as a model systems of lipid bilayers to investigate the interaction peptide and the membranes [42; 45]. The DMPC/DHPC=1:1 forms a small discoidal membrane that is able to avoid a light scattering of lipid systems. The spectral patterns show that both peptides mainly formed \(\alpha\)-helices in DMPC/DHPC bicelles [43]. However, the magnitude of molar ellipticity of the positive band at 195 nm and the negative bands at 210 nm and 225 nm of D-Phes was lesser than those of L-Phes. This indicated that the helical content of D-Phes is slightly lesser than that of L-Phes, which could be due to the presence of D-Phe2 residue in D-Phes.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Peptides** & **Ka/M\({}^{-1}\)** \\ \hline L-Phes & \(4.7\times 10^{6}\) \\ D-Phes & \(7.3\times 10^{6}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The binding constants Ka of peptides to 1,2-dimyristoyl-_sn_-glycero-3-phosphocholine (DMPC) using quartz crystal microbalance (QCM).
# Stochastic Network Calculus-Aided Delay Analysis of Wireless-Power Line Mixed Networks
## 4 Numerical Results  
In Figure 1, we investigate the impact of \(\delta\) and \(\dfrac{P_{w}}{\sigma^{2}}\) on the delay bound under different delay violation probabilities. Other system parameters are set as \(B=\ln 2\) MHz, \(\rho=0.9\), \(K=20\) and \(\dfrac{P_{p}}{\sigma_{8}^{2}}=7\) dB. We observe that, with the increase of \(\delta\), the delay bound becomes worse. This is due to the fact that more traffic arrivals mean more queue congestion, which will lead to a worse delay bound. Furthermore, for \(\dfrac{P_{p}}{\sigma_{8}^{2}}\), a high value of SNR indicates a better transmission environment and a lower delay bound.  
Figure 1: Delay versus violation probability under various data flows and \(\dfrac{P_{w}}{\sigma^{2}}\).  
In Figure 2, the parameter \(\delta\) of the Poisson arrival flow is given in 100 kbps. We set the average SNR of the wireless link as \(\frac{P_{g}}{\sigma^{2}}=5\) dB. For the PLC link, the parameters are given as \(\mu_{h_{p}}=1\) and \(\sigma_{h_{p}}^{2}=1\). We conducted an experiment for the delay bound versus various average SNR of PLC link, under the different system parameters \(K\) and \(\rho\), where \(K\) is the impulsive noise index and \(\rho\) is the probability of the impulsive component of the noise. From the results, we observe that, with the increase of the value of \(K\), the delay bound for the system becomes terrible. This phenomenon can be reasonably expressed from Equation (9). When parameter \(K\) enlarges, the service capability of the PLC link under the circumstances of impulsive noise becomes weak as shown in Equation (6).  
This also causes a decrease of the overall service capability according to Equation (9) and makes the delay bound larger. This expression also makes sense for the change of delay under different \(\rho\). For other parameters, such as the data rate \(\delta\) and average SNR \(P_{p}/\sigma_{g}^{2}\), the impacts on the performance can be also evaluated via Figures 1 and 2. We observe that the delay bound increases with the increase of the data rate. This is reasonable since a higher data rate means more traffic congestion, which will result in a higher delay level. Similarly, a larger value of \(P_{p}/\sigma_{g}^{2}\) indicates a better transmission environment and a lower delay bound.
# Co-Simulation of Multiple Vehicle Routing Problem Models
## 3 The Designed Model
### Description of Atomic VRP Models
#### 3.1.1 First VRP Model  
We use ant colony optimization for the VRP, where the colony of ants is created from the depot. Each Ant constructs a route for vehicles that will serve the clients starting from the depot and returning to the same depot. The principle of the routing algorithm is described below, see Algorithm 1.  
```
0:\(R\), ant_colony, \(Q(k)\);
0:route_map;
1:for(\(a\in ant\_colony\)) do
2:route_map \(\gets r_{0}\);
3:\(R\gets R-r_{0}\);
4:\(k\gets 0\);
5:\(Q(k)\gets 0\);
6:\(i\gets r_{0}\);
7:while\(R\
eq\emptyset\)do
8:for(\(r_{j}\in R\)) do
9:\(p_{ij}\leftarrow\textit{probability\_of\_point\_to\_choose\_point}\);
10:endfor
11:\(r_{s}\gets r_{j}\_{\textit{with\_highest\_p}_{ij}}\);
12:\(Q(k)\gets Q(k)+\textit{demand}(r_{s})\);
13:if\(Q(k)>\textit{capacity\_of\_vehicle}\)then
14:\(k\gets k+1\);
15:\(Q(k)\gets 0\);
16:\(route\_map\gets route\_map+r_{0}\);
17:\(i\gets r_{0}\);
18:else
19:\(route\_map\gets route\_map+r_{s}\)
20:\(i\gets r_{s}\)
21:\(R\gets R-r_{s}\);
22:endif
23:endwhile
24:endfor
25:return\(route\_map\_of\_the\_best\_ant\_solution\);
```  
**Algorithm 1** Routing and sequencing of the first model  
The improvements of the solution shown in Algorithm 1 reached in successive iterations. In each iteration all ants in the colony travel between a pair of clients. The probability of an ant to visit an unvisited client is calculated based on the pheromone and the distance and a client will be chosen according to this probability. Then the ant checks if it can visit the given client, i.e., if its current load allows it to add the demand of the client and verify the vehicle's capacity violation. If yes, the ant visits the specified client (adding client's demand and indicating it as selected). Otherwise, the ant ends the current trip with empty load (returning to its colony) and the algorithm restarts a new trip. We apply the following equations for a client \(i\) to choose the next client \(j\)  
\[p_{ij}=\frac{(\tau_{ij})^{\alpha}\times(\eta_{ij})^{\beta}}{\sum_{l\in R}(\tau _{il})^{\alpha}\times(\eta_{il})^{\beta}} \tag{6}\]  
\[\eta_{ij}=\frac{1}{cost_{ij}} \tag{7}\]  
where \(p_{ij}\) is the probability of the point \(i\) to choose point \(j\), \(\tau_{ij}\) is the strength of pheromone trail between point \(i\) and \(j\), \(\alpha\) is the coefficient that controls the influence of the pheromonetrail \(\tau_{ij}\), \(\beta\) is the coefficient that controls the influence of the visibility \(\eta_{ij}\), and \(R\) is a set of clients.  
At the end of each iteration, the pheromone trail evaporates according to the following equation:  
\[\tau_{ij}=(1-\rho)\times\tau_{ij} \tag{8}\]  
where \(\rho\) is the coefficient of the pheromone evaporation and the update state is calculated by applying the next equation:  
\[\tau_{ij}=\tau_{ij}+\Delta\tau_{ij} \tag{9}\]  
where  
\[\Delta\tau_{ij}=\frac{\mathit{best\_known\_solution}}{\mathit{best\_global\_ cost}} \tag{10}\]
# Active Optics and Aberration Correction Technology for Sparse Aperture Segmented Mirrors
## 3 Correction Quantity-Solving Method  
In this section, an improved generalized ridge estimation is proposed to solve for the correction quantities. These quantities are required in the mirror correction by the segmented mirror co-phase maintenance and WH techniques. These correction quantities are also needed to solve for the ridge and weight matrix in the iterative process using an improved differential evolution algorithm.  
Figure 3: Schematic diagram of the calibration process.
## 2 Materials and Methods  
### Experimental Setup  
Testing of two BHMV with different diameters (23 mm and 25 mm) for one model (St Jude Medical, St. Paul, MN, USA), for one BMHV for another model (ONX, CryoLife, GA, USA--29 mm diameter), and for BMHV dysfunction as a blocked leaflet was performed. A summary of the tested valves can be found in Table 1.  
Details of the MRI-compatible in-vitro system used for this experiment can be found in the study of [PERSON] et al. [13] (see Figure 1). Briefly, a pulsed pump is placed outside the MRI environment. The valve is fixed in two cylindrical parts by two plastic supports, enabling the suture annulus of the valve to be held and providing sufficient space after the valve for the blood to flow in a similar shape to that in the aorta (progressive reduction of the diameter as in vasalva sinus while not reproducing the anatomy of the sinus due to using a cylindrical reduction symmetrical in all directions).  
The fluid used to mimic the blood is a water glycerol solution based on the work of [PERSON] [14]. The pump flow command was set to target a 5 L/min cardiac output, and the pulsed flow profile was fixed as a physiological aortic flow-shaped waveform with a 70 bpm frequency. The pressure was measured using a PR21 SR/804443-1 pressure sensor  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c}  & & & & & & & & & & & & & & & \\
**BMHV Model** & & & & & & & & & & & & & & & \\
**(Condition)** & & & & & & & & & & & & & & & \\ \hline ONX29 & OnX (normal) & 0.781 & 2.5 & 29 & 23.4 & 14.2 & 90 & 300 & 20.2 & 12.1 & 0.60 & 1.74 & 5 (12.5) & 0.61 \\ SJM23 & & \begin{tabular}{c} St Jude Medical (normal) \\ \end{tabular} & 0.781 & 2.5 & 23 & 21.4 & 12.5 & 85 & 280 & 18.9 & 16.9 & 0.89 & 1.4 & 3 (7.5) & 0.70 \\ SJM25 & \
\begin{tabular}{c} St Jude Medical (normal) \\ \end{tabular} & 0.781 & 2.5 & 25 & 23 & 13.9 & 85 & 260 & 23.4 & 32.7 & 1.40 & 1.55 & 2 (5) & 1.06 \\ SJM25B &
\begin{tabular}{c} St Jude Medical (blocked) \\ \end{tabular} & 0.938 & 3 & 25 & 23 & 13.9 & 85 & 310 & 21.4 & 25.4 & 1.19 & 1.01 & 4 (12) & 0.73 \\ \hline \end{tabular}
\end{table}
Table 1: MRI acquisition settings and results on the 4 tested BMHV models: MRI 4D Phase-Contrast flow sequences characteristics, blurring distances and EOA computation. VENC: velocity encoding.  
Figure 1: Schematic of the experiment, adapted from [13] with gear pump, pressure measurement, and set up outside of the MRI. PC: Personal computer.  
(Keller AG, Winterthur, Switzerland) and National Instrument acquisition card (National Instrument, Austin, TX, USA) both before and after the prosthesis.  
Tested BMHVs are summarized in Table 2. The OnX model has a 90\({}^{\circ}\) leaflet opening, a height of 14.7 mm (label 21) and 17.8 mm (label 29), and the shape of the hinge region is an 'eight' or infinity symbol shape. The St Jude Medical model has an 85\({}^{\circ}\) leaflet opening, a height of 12.5 mm to 13.9 mm, and a hinge region shaped like a round butterfly (Figure 2). A simulation of BMHV dysfunction was performed by blocking one of the leaflets of the bi-leaflet valve with a suture wire without preventing the closing of the other leaflet.