esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Solution Polymerization  
PAA and copolymers of AA and HEMA with different molar ratios between comonomers were prepared via solution polymerization using KPS as initiator. Three AA/HEMA molar ratios were considered: 18/1, 9/1, and 3/1 denoted by **PAH18**, **PAH9**, and **PAH3**, respectively. The final concentration for the polymer solution was about 10 wt.% to avoid copolymer precipitation, especially at higher content of HEMA. The monomer or mixture of monomers was placed into a round bottom reactor equipped with a mechanical stirrer, refrigerant, and an inlet for argon feed. The reactor temperature was maintained constant by using a water bath. After bubbling argon for about 30 min for air removal, the temperature was raised to 70 \({}^{\circ}\)C and 0.037 M KPS water solution was injected under mechanical stirring to initiate polymerization. The reaction was carried out for 3 h at the same constant temperature. For **PAH3** system, the obtained copolymer was no longer soluble in water and precipitates.  
After cooling the polymer solutions, the unreacted monomers and initiator were removed by dialysis against distilled water, using dialysis tubing with a molecular mass cut-off of 14,000 Da. The final solution concentrations were determined gravimetrically. Three samples from each polymer solution were weighed and placed in an oven at a temperature of 90 \({}^{\circ}\)C for water evaporation. After the samples reached a constant mass, their concentrations were calculated, and the final value was the mean of the three measurements.  
Samples with concentration of approximately 0.25% were prepared by dilution with distilled water and then titrated with a strong base, NaOH. The acid-base titrations were monitored potentiometrically using a pH glass electrode connected to a multi-parameter analyzer Consort C861.  
Intrinsic viscosities, [n], were determined from capillary viscosity measurements, at 25 \(\pm\) 0.1 \({}^{\circ}\)C, using an Ubbelohde visometer. The particular concentrations of the samples prepared by dilution were chosen to give specific viscosities below 0.7, all the solutions being adjusted to pH 3 using HCl 1N.
# Confocal Laser Endomicroscopy for Bladder Cancer Detection: Where Do We Stand?
###### Abstract  
Introduction: Confocal laser endomicroscopy (CLE) is a relatively new technology that allows for a real-time in situ microscopic characterization of tissue lesions, being able to discriminate between low- and high-grade ones. After a first period of slow diffusion caused by technological limitations and elevated costs, CLE applications are rapidly spreading in different branches of medicine, and there is mounting evidence of its advantages for the management of different tumors such as bladder cancer (BCa), from both a diagnostic and a clinical point of view. In this systematic review (SR), we evaluate the state-of-the-art CLE for BCa management. **Material and methods:** We performed an SR and quality assessment analysis of the current literature in this regard following the PRISMA guidelines. All data were independently verified by two different authors and discrepancies were solved by a third author. Moreover, a quality-assessment analysis according to QUADAS-2 criteria was performed to evaluate the studies selected for SR. **Results:** A total of 158 articles were retrieved, of which 79 were rejected and 38 were removed as duplicates. After article selection, seven prospective studies were assessed for data extraction. These accounted for 214 patients overall, with a correspondence rate between CLE and histopathological examination ranging from 54.6 to 93.6%. Regarding quality assessment, three out five prospective studies have at least a high risk of bias in one QUADAS-2 domain, whereas the applicability always has a low risk of bias. **Conclusion:** Despite actual technical limitations, the preliminary results of this appealing technology are encouraging and should prompt further investigations.  
non-muscle invasive bladder cancer; cystoscopy; confocal laser endomicroscopy; endourology
## 3 Methods
### Series and Parallel Combinations  
Consider the series combination of two SubNets: \(A+B\). Let SubNet \(A\) have \(L_{A}\) convolution layers that follow Equation (2), and SubNet \(B\) has \(L_{B}\). The combined network would have a total of \(L=L_{A}+L_{B}+1\) layers, where the final layer is the one fully connected layer as shown in Equation (4). The parameters for SubNet \(A\) are  
\[\phi_{A}=\{\mathbf{W}^{l},\mathbf{b}^{l}|l\in\{1,2,\ldots,L_{A}\}\}. \tag{11}\]  
These are fixed after the training for SubNet \(A\). The parameters for the SubNet \(B\) convolution layers, plus the fully connected layer are given by  
\[\phi_{B+}=\{\mathbf{W}^{l},\mathbf{b}^{l}|l\in\{L_{A}+1,L_{A}+2,\ldots,L_{A}+ L_{B}+1\}\}. \tag{12}\]  
The parameters in \(\phi_{B+}\) are updated during the training of \(A+B\). This output of the series layers goes to the softmax layer as before using Equation (5). This scenario is illustrated in Figure 7a.  
Next, consider two parallel SubNets: \(A\)\(||\)\(B\). Again, let SubNet \(A\) have \(L_{A}\) convolution layers that follow Equation (2), and SubNet \(B\) has \(L_{B}\). Let us define the convolution layer parameters for each SubNet as  
\[\phi_{A}=\{\mathbf{W}^{l}_{A},\mathbf{b}^{l}_{A}|l\in\{1,2,\ldots,L_{A}\}\} \tag{13}\]  
and  
\[\phi_{B}=\{\mathbf{W}^{l}_{B},\mathbf{b}^{l}_{B}|l\in\{1,2,\ldots,L_{B}\}\}. \tag{14}\]  
The output of the SubNet \(A\) convolution layers is given by  
\[\mathbf{x}^{l}_{A,n}=g(\mathbf{W}^{l}_{A}\mathbf{x}^{l-1}_{A,n}+\mathbf{b}^{l }_{A}), \tag{15}\]  
where \(l=1,2,\ldots,L_{A}\). The output of the the SubNet \(B\) convolution layers is given by  
\[\mathbf{x}^{l}_{B,n}=g(\mathbf{W}^{l}_{B}\mathbf{x}^{l-1}_{B,n}+\mathbf{b}^{l }_{B}), \tag{16}\]where \(l=1,2,\ldots,L_{B}\). Note that the inputs to the two parallel SubNets are the same so that we have \(\mathbf{x}_{A,n}^{0}=\mathbf{x}_{B,n}^{0}=\mathbf{x}_{n}\). Let the fully connected output layer be designated as Layer \(L=\text{Max}(L_{A},L_{B})+1\). The output of this fully connected layer with the final feature maps concatenated is given by  
\[\mathbf{x}_{n}^{L}=\mathbf{W}_{A||B}\left[\begin{array}{c}\mathbf{x}_{A,n}^{ L_{A}}\\ \mathbf{x}_{B,n}^{L_{B}}\end{array}\right]+\mathbf{b}_{A||B}. \tag{17}\]  
This output goes to the softmax layer as before using Equation (5). The parameters in \(\phi_{A}\) are fixed and the parameters in \(\phi_{B}\) along with \(\mathbf{W}_{A||B}\) and \(\mathbf{b}_{A||B}\) are updated. This scenario is illustrated in Figure 7b.
## 4 Results
### Gear Tooth Sample
#### 4.1.2 Gaussian Fit  
Using the Gaussian fit approach introduced in Section 3, we can overcome the discrete distribution and get the result shown in Figure 8a. The output now shows an even smoother progression with similar outliers at the gear flank edge. Using the amplitude of the phase gradient direction vectors, shown in Figure 8b, we get information about the signal quality, which can be used for weighted filtering.  
Gaussian fits based on the sampling points, as described in Section 3, are plotted in Figure 6. The dashed-line shows the initial Gaussian fit guess for each point plotted in Figure 8a. The final Gaussian fits are shown as solid lines and are in very good agreement with the reconstruction distances.  
Applied to all pixel values, both focus plane selections result in a continuously focused measurement, as shown in Figure 9.  
Now instead of using a very large synthetic wavelength, the shape-from-focus output can be used as rough estimation of the object's surface shape for the combination of wavelengths. By combining the shape-from-focus data with all three synthetic wavelengths, we get the focused unambiguity-extended 3D results in Figure 10. However, due to the discrete progression of the shape-from-focus estimation, we get various combination errors applying finer wavelengths to the maximum filtered input data  
Figure 8: Shape-from-focus output including phase smoothness \(s_{z}\) as a quality criterion, high values show a strong result; **(a)** focus map \(z_{opt,Gauss}\) reconstructed by pixel wise shape-from-focus Gaussian fit. The \(z\) sections at colored crosses are illustrated in Figure 6; **(b)** optimized values of \(s_{z}\) using the Gaussian approach.  
Figure 9: **(a)** All-in-focus phase map \(\varphi\)–the whole measurement shows sharp stripes and minimized phase noise. In the 3072 \(\times\) 3072 pixels original image, the fringes are clearly resolved; **(b)** Magnified view of the red area of **(a)**. The fringe spacing is 10 pixels.  
as shown in Figure 10a. Maximum filtered height estimation data works perfectly for all-in-focus calculations as shown in Figure 10 but has to be generated with very small propagation step width \(z\) in order to suffice as a rough synthetic starting point for wavelength combination. Since this is very time-consuming, the Gaussian fit algorithm should be used instead here.  
Applying all the finer wavelengths on the inverse of the Gaussian filter output, we get the focused unambiguity-extended 3D result in Figure 10b. The combined data considerably exceeds the geometrical ambiguity and focus limits, and is almost free of combination errors. \(s_{z}\) is used in both of the height maps to mask out data points, but the shape-from-focus algorithm could not reconstruct safely. Masked data points are shown as white points.
# A Novel Cloud Approach for Connected Vehicles
## 4 Analytic Model
### Discussion  
In this section, we present a framework in which we can express the behavior of different ICN approaches. This framework takes into account the network topology and the mobility of the vehicles. Additionally, we propose a model for the propagation of the messages between vehicles over the network. Then, we focus on two performance indicators which are the network overhead related to the ICN exchanges and the data freshness. The framework definition allows us to give the analytic model of any ICN approach in order to compare them, as we did with two intermediate versions of our GeoVCDN approach. These versions have been introduced in order to see the benefits of each mechanism introduced in GeoVCDN. In the next section, we compare the different approaches based on the analytic models and the results obtained with a simulator we designed.
## 4 Simulations and Results
### Security Features of Proposed System
#### 4.6.5 Availability  
In our proposed system, data are stored in IPFS in a distributed manner. A distributed hash table is maintained against the data stored in IPFS. Whenever data are required, a request of data are sent to a specific node at which data are placed. Due to the distributed storage, the availability of data is achieved while ensuring high throughput of the system.
# The Applications of MALDI-TOF MS in the Diagnosis of Microbiological Food Contamination
## 5 Future Perspectives
### Regulatory Acceptance and Standardization  
For MALDI-TOF MS to realize its full potential in food microbiology, broader regulatory recognition and harmonized guidelines are essential. The future will likely see the development of international standards for MALDI-TOF MS in food testing, akin to those established for molecular diagnostics. Collaborative validation studies, led by entities such as the International Organization for Standardization (ISO), EFSA, and U.S. FDA, will be crucial in defining performance criteria, reproducibility metrics, and minimum database requirements. Training programs and professional certification pathways for MALDI-TOF MS in food laboratories will also support workforce readiness and method adoption [39; 72; 155; 156].
# The Applications of MALDI-TOF MS in the Diagnosis of Microbiological Food Contamination
## 2 The Principles of MALDI-TOF MS
### Broad Taxonomic Coverage:  
A single MALDI-TOF MS platform can identify a wide range of microorganisms--including Gram-positive and Gram-negative bacteria, yeasts, molds, and even some filamentous fungi--without the need for organism-specific primers or biochemical panels. This universal applicability eliminates the need for multiple parallel assays and reduces the risk of overlooking unexpected contaminants [46; 85; 90].  
In mixed-product environments such as dairy, meat, and fermented foods, this breadth is especially useful for detecting both pathogens and quality-relevant microbes (e.g., _Lactobacillus, Saccharomyces_), providing a comprehensive microbiological profile from a single-assay platform [33; 55; 58].
# Performance Prediction and Design of Stratospheric Propeller
## 2 Methodology
\[f=\frac{N_{B}}{2}\frac{1-\overline{r}}{\sin\varphi_{0}} \tag{15}\]  
An iterative method is used to calculate the axial and tangential induction factors \(a\) and \(a^{\prime}\): (1) set the initial value of \(a_{0}\) and \(a_{0}\)'; (2) calculate the angle of actual flow \(\varphi\); (3) according to the pitch angle \(\theta\), calculate the angle of attack \(a\); (4) obtain the \(C_{L}\) and \(C_{D}\) that form the lift and drag curves of the airfoil respectively; (5) use Equation (8) to calculate the \(C_{T_{V}}\) and \(C_{F_{V}}\); (6) the \(a_{t}\) and \(a_{t}\)' are calculated by Equation (11); (7) comparing \(a_{0}\) and \(a_{t}\), \(a_{0}\)' and \(a_{t}\)', if they are beyond the allowable error range, repeat steps (2) to (7); otherwise, the final \(a\) and \(a^{\prime}\) are generated, and the iteration is ended. The iterative process is shown in Figure 2.  
The thrust and torque of the propeller are obtained by:  
\[\left\{ \begin{array}{l}T=\frac{1}{2}\rho V_{\infty}^{2}N_{B}\int_{R_{\rm turb }}^{R}b\;\frac{(1+a)^{2}}{\sin^{2}\varphi}C_{T_{V}}dr\\ M=\frac{1}{2}\rho V_{\infty}^{2}N_{B}\int_{R_{\rm turb}}^{R}b\;\frac{(1+a)^{2}} {\sin^{2}\varphi}C_{F_{V}}rdr\end{array} \right. \tag{16}\]  
Figure 2: Iterative process for propeller performance calculation. The dimensionless parameter thrust coefficient \(C_{T}\), the power coefficient \(C_{P}\), and the efficiency \(\eta\) are defined as follows:  
\[\left\{ \begin{array}{c}C_{T}=\frac{T}{\rho m_{S}D^{4}}\\ C_{P}=\frac{2\pi n_{s}M}{\rho n_{s}^{3}D^{5}}\\ \eta=\frac{TVs_{M}}{2\pi n_{s}M}=\frac{C_{T}}{C_{P}}\lambda\end{array} \right. \tag{17}\]  
where \(\lambda\) is the advance ratio, given by:  
\[\lambda=\frac{V_{\infty}}{n_{s}D} \tag{18}\]
## 3 Results
### Optimization of Wine Yeast Identification by MALDI-TOF MS
#### 3.1.2. **O**enological Laboratory Specific Database of MALDI-TOF MS  
The neurological laboratory specific Biotyper database was created with yeast species and strains specific to the wine environment. Yeast strains originating from must and wine were provided by CRB OENO. For all isolates, species identification was validated by 26S rDNA sequencing, and the sequence and match information on BLAST are presented as Table 1. To implement the database, different situations were considered: (i) when strains of a given species were present in the Bruker database and its identification result was <2.0, only one or two strains isolated from the wine environment were approved in the database (as for example for _Hanseniuspora uvarum_ or _Lachancea thermotolerans_), (ii) when species were not present in the database (_Shizosaccharomyces japonicus_ or _Starmerella bacillaris_), only one or two strains were added, leading to an identification score >2.  
For _Saccharomyces cerevisiae_, the scores with wine isolates were not correct (<2.0), and five additional _S. cerevisiae_ strains originating from a wine environment were therefore added, and the resulting identification scores were found to be greater than 2 using these two databases. For _Pichia guilliermondii_, 14 strains were already present in the Biotyper database, while the identification results were >2.0 for wine isolates; only one strain of _Pichia guilliermondii_ related to the wine environment was added. Considering the genetic and phenotypic diversity of _Brettanomyces bruxellensis_[46], 15 strains representative of the genetic groups of the species were added. Finally, 17 distinct species and 43 different strains were added to the existing database (Table 1).  
Figure 1: Percentage of clones given identical identification by 26S rDNA sequencing and MALDI-TOF MS (the number of clones considered is given in brackets).
## 4 Results and Discussion
These results all prove that the overheating problem really exists in the actuator. According to the theoretical analysis presented in Section 3, the temperature rise can be controlled by the driving voltage and duty ratio. Therefore, a study was carried out to characterize the influence of voltages and the duty ratio on the temperature rises of the actuator. Figure 5: Performance of the actuator on the permanent magnet orbit: (**a**) the motion process; (**b**) speed versus applied voltage; (**c**) speed versus magnetic induction intensity. Figure 6: Temperature rise experimental results of the actuator working in the endoscopic channel when the driving voltage is 200 VPP in the first 3 min. Figure 8 shows the dependence of the speed and temperature rises on the different duty ratios when the driving voltage is 200 V\({}_{\text{PP}}\) and the magnetic induction intensity of the permanent magnet orbit is 43 mT. As the duty ratio increases, the energy input to the actuator increases correspondingly, resulting in a corresponding increase in the actuator's speed, which shows an obvious linear relationship with the duty ratio. The speed of the actuator is 41 mm/s when the duty ratio is 100%. When the duty ratio is adjusted to 10%, the actuator speed is greatly reduced to only 2.3 mm/s. With a further decreasing duty ratio, the actuator speed will continue to decrease to interrupt the working stability of the actuator. Meanwhile, an upward trend in the temperature with an increasing duty ratio can be seen. As compared with a 100% duty ratio operation mode, a 60% duty ratio can more effectively decrease the temperature rise, as revealed by the corresponding temperatures of 44.4 to 41.4 \({}^{\circ}\)C, respectively. When the duty ratio is adjusted to 30% or less, the temperature rises are greatly reduced to only 36.6 \({}^{\circ}\)C, which is close to the initial temperature (36.4 \({}^{\circ}\)C). Therefore, adjusting the driving voltage duty ratio has been proven to be a feasible method for preventing the temperature rising issue of the actuator in the biopsy channel with only a slight sacrifice of the actuator speed. It is noteworthy that the amplitude of the leg is too small and it is too difficult for the actuator to work stably when the driving voltage is lower than 200 V\({}_{\text{PP}}\) with a magnetic induction intensity of 43 mT, so the temperature rise control method that adjusts the driving voltage is not practical under this circumstance. Figure 8: Speed (bar) and temperature rises (line) versus duty ratio performance of the actuator on the permanent magnet orbit. Figure 7: Temperature rise calculated results of the actuator working in the endoscopic channel when the driving voltage is 200 V\({}_{\text{PP}}\) in the first 3 min: (**a**) temperature field distribution; (**b**) a comparison of the simulation results and experimental results.
# A Message Passing-Assisted Iterative Noise Cancellation Method for Clipped OTFS-BFDM Systems
## 5 Simulation Results
### CCDFs of the PAPR for OTFS-BFDM Signals with Different \(\gamma\)  
Figure 4 shows the CCDFs of the PAPR for OTFS-BFDM signals with different clipping ratios \(\gamma\). As shown in Figure 4a, the PAPR of OTFS-Rect signal is unbearable high. After clipping, the PAPR can be significantly reduced. The \(PAPR_{0}\) of non-clipped OTFS-Rect signals is 11.5 dB at \(CCDF=10^{-3}\). After clipping with \(\gamma=3\) dB, the \(PAPR_{0}\) can be reduced by 4.7 dB. Moreover, the smaller the clipping ratio, the more significant the PAPR decreases since the clipping ratio is proportional to the clipping threshold. A similar conclusion can be drawn from Figure 4b.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Path Index** & **1** & **2** & **3** & **4** & **5** \\ \hline Delay (\(\mu\)s) & 0 & 2.08 & 4.17 & 6.25 & 8.33 \\ Doppler\_1 (Hz) & 0 & 0 & 0 & 483.87 & 967.74 \\ Doppler\_2 (Hz) & 0 & 0 & 483.87 & 967.74 & 1451.61 \\ Doppler\_3 (Hz) & 0 & 483.87 & 967.74 & 1451.61 & 1935.48 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Delay-Doppler profile for the channel model with path = 5.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Carrier frequency (GHz) & 4 \\ Sub-carrier spacing (kHz) & 15 \\ Frame size (\(M\), \(N\)) & (32, 31) \\ Modulation scheme & 4-QAM \\ CP (\(\mu\)s) & 10.42 \\ Channel estimation & ideal \\ Oversampling factor & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation parameters.
# Image-Based Gimbal Control in a Drone for Centering Photovoltaic Modules in a Thermal Image +
## 3 Implementation of Gimbal Control through PV Image Analysis
### Implementation of Object Recognition Image Processing Program  
The program is implemented by OpenCV and the C++ language, which is used in the Linux Codeblocks environment. The implementation process of the image processing algorithm is as follows. We convert RGB images to Hue-Saturation-Value (HSV) images. The HSV color space is more suitable for object detection than the RGB color space. The RGB color space is a combination of colors that reduces the intuition when a specific color must be expressed. The RGB color space is not intuitive. However, in the HSV color space, intuitive native colors can be analyzed [18]. The PV module can be detected with the color of the blue hue area in the HSV color space when the PV module is detected. We extract the PV module area using OpenCV inRange function. The inRange function extracts pixels corresponding to the blue region in the HSV color space.  
In extracting the PV module area, blue pixels can be extracted in some background areas as noise. To eliminate this kind of noise, dilate/erode and medianBlur function were applied. Then, we binarize the given PV module area, and using the Hough transform, we calculate the horizontal angle. The Hough transform is used in automated analysis of digital images. All PV modules contain white lines in a specific grid-like pattern. These lines are the distinctive feature of a PV module. The Hough transform is a popular feature extraction technique widely used to detect lines in images. The Hough transform detects a line represented by Equation (2).  
\[r=x\text{cos}\theta+y\text{sin}\theta \tag{2}\]  
where \(r\) represents the distance from the origin to closest point on the straight line, and \(\theta\) represents the angle between the \(x\) axis and the line connecting the origin with that closest point.  
Each line in the image is associated with a pair (\(r\), \(\theta\)). For each pixel at (\(x\), \(y\)) in an edge image, the Hough transform finds the set of all straight lines that passes through this point. The final output of the Hough transform is the accumulator matrix. This accumulator matrix contains the quantized values of \(r\) and \(\theta\). The element with the highest value indicates the most represented straight line in the image. Therefore, the value of \(r\) and \(\theta\) is determined by referring to the existing information among the three values representing the highest value.  
Moreover, we calculate the vertical position with the center of gravity of the block. We show the results for an implemented image processing example in Figure 10: original visible image (a), inRange function (b), threshold function (c), Hough line function (d), bold function (e), and derived result (f).