esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review on Microfluidic Platforms Applied to Nerve Regeneration
## 2 Design of Microfluidic Devices
### Materials
In addition, metals such as electrode resistors are difficult to deposit on PDMS, which limits the integration of resistive electrodes. However, PDMS can be bonded to glass slides by plasma treatment and dielectric deposition can be performed on glass slides. In summary, PDMS is an ideal material for microfabrication of the microfluidic device for cell culture and nerve regeneration research. In recent years, hydrogels have been becoming popular materials in microfluidic devices. There are various types of hydrogels, and most of hydrogels are hydrophilic, such as agaros, polyacrylamide and alginate. Some hydrogels are hydrophobic association hydrogels, such as hydrophobic collagen and synthetic hydrogels using molecules with hydrophobic properties as segments [34; 35; 36]. In addition, there are hydrogel materials that have both hydrophilic and hydrophobic features. For example, [PERSON] [37] presented a new type of hydrogels combining both hydrophilic and hydrophobic structures as a replacement for cartilage [PERSON] [38] proposed a hydrosol using acrylamide as a hydrophilic monomer and lauryl acrylate as a hydrophobic monomer. The advantage of adopting hydrogels for cell culture is the 3D network structure of hydrophilic polymer chains that allows the diffusion and transmission of small molecules and biological particles. Hydrogels can be used to construct microchannels for the transport of solutions, cells and other materials [26]. Hydrogel is an ideal material to create chemical and physical gradients. For example, collagen, agarose and poly ethylene glycol are preferable for generating chemical gradient in microenvironments due to their permeability allowing fluid diffusion. Besides this, by placing different concentrations of hydrosols, such as collagen, in adjacent chambers, physical gradients can be created, which can determine the direction of cell migration and growth [39]. However, there are some disadvantages of hydrogels for neural cell culture, such as unfavorable diffusion of nutrients and poor permeability, which leads to necrosis of cells in deep layers due to starvation and lack of oxygen [40]. Due to the low density and low strength at the macromolecular scale, hydrogels support only micron scales lower than the nanoscale of other polymers in microfabrication. The properties of microfluidic materials discussed above are listed in Table 1.
# Continuous-Variable Quantum Key Distribution Robust Against Polarization-Dependent Loss
## 6 Conclusions  
We have proposed a PDM-based CVQKD system over the optical fiber channel, where the key information could be encoded independently by the optical fields \(E_{x}\) and \(E_{y}\). By applying the PDM scheme in the system, the 2-dimensional modulation and polarization multiplexing achieve two-fold channel capacity without the need of additional bandwidth resources. Nevertheless, the crosstalk  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Symbol** & **Meaning** \\ \hline \(T_{0}^{k}\) & the equivalent transmittance of quantum channel after PPC scheme \\ \(\epsilon_{0}^{k}\) & the equivalent excess noise of quantum channel after PPC scheme \\ \(\epsilon_{pll}\) & the excess noise due to lack of orthogonality \\ \(\epsilon_{ab}\) & the error rate between Alice and Bob after coherent detection \\ \hline \hline \end{tabular}
\end{table}
Table 1: The main parameters of the CVQKD protocol in the security analysis.  
Figure 10: The secret key rate as a function of transmission distance.  
Figure 9: Parameter relationships in the PDM-based system. (**a**) The secret key rate as a function of modulation variance \(V_{M}\) in different excess noise with transmission distance d = 100 km. (**b**) The secret key rate as a function of excess noise in different modulation variance \(V_{M}\) with transmission distance d = 100 km.  
induced by the PDL of each polarization causes the signal distortion, leading to an imbalanced optical signal-to-noise ratio for two polarizations. In order to mitigate the performance decrease, we adopt an improved PPC scheme to overcome the PDL problem. The theoretical analysis shows the performance of the scheme and demonstrates its availability in the system. By selecting the original information symbols and interleaving the \(X\) and \(P\) components between two polarizations of a given channel, the decoded signals of two polarizations can achieve a lower error rate. In addition, the numerical simulation results indicate that the required SNR is lower than that of the uncoded method when the same error level is reached in the PPC scheme. Besides, the proposed method enhances the overall system performance and is robust against a wide range of PDL without any coding overhead. It reduces the PDL-induced error rate, and thereby increases the secret key rate of the PDM-based CVQKD system.  
Conceptualization, Y.G. and M.C.; writing--original draft, Y.G. and M.C.; writing--review and editing, D.H.  
This work was supported by the National Natural Science Foundation of China (Grant Nos. 61572529, 61871407, 61801522).  
The authors declare no conflict of interest.
## 2 Super-Operator Integral Equation for Scattering of Quantum Light by a Dielectric Body
### Preliminaries
We made the transformation \(\Gamma\big{(}\mathbf{x},\mathbf{x}^{{}^{\prime}};v\big{)}\to\Gamma\big{(} \mathbf{x},\mathbf{x}^{{}^{\prime}};k\big{)}\) in Equation (5) (and will use it in future) because wavenumber will play the role of a variable of integration in different relations. One can find the short reminder of resolvent theory for the classical Fredholm equations in Appendix A. If the inside field is known, the outside field may be found via the integration over the body volume  
\[\tilde{U}_{k}(\mathbf{X})=\tilde{U}_{k0}(\mathbf{X})-k^{2}(\varepsilon-1) \int\limits_{S}g\big{(}\mathbf{X},\mathbf{x}^{{}^{\prime}}\big{)}\tilde{U}_{k} \big{(}\mathbf{x}^{{}^{\prime}}\big{)}d\mathbf{x}^{{}^{\prime}}, \tag{6}\]  
where \(\mathbf{X}\
ot\in S\). For the negative frequency component, the equation may be found via the Hermitian conjugation of Equation (6). Equations (5) and (6) look like the complete solution of the scattering problem: they satisfy the Helmholtz equation over the whole space, boundary conditions and radiation conditions. In contrast to the classical case, we need to add the commutation relation, which reflects the bosonic origin of the EM field. The commutation relation is local; thus, it may be used in the ordinary form for homogeneous medium. As shown in Appendix B, it reads  
\[\big{[}\tilde{U}_{k}(\mathbf{x}),\tilde{U}_{k^{{}^{\prime}}}^{+}\big{(}\mathbf{ x}^{{}^{\prime}}\big{)}\big{]}=\frac{\hslash k^{2}c}{\pi\varepsilon( \mathbf{x})}\operatorname{Im}\big{(}g\big{(}\mathbf{x},\mathbf{x}^{{}^{\prime }}\big{)}\big{)}\delta\big{(}k-k^{\prime}\big{)} \tag{7}\]  
In fact, it is obtained from the relation for homogeneous dielectric space by replacing \(\varepsilon\to\varepsilon(\mathbf{x})\).
## 3 Results and Discussion
### Synthesis and Evaluation of the Anti-staining Coating Material
#### 3.1.2 Evaluation of Water and Oil Repellency  
Water and n-hexadecane contact angles and friction coefficient are summarized in Table 1. As can be seen in Table 1, **3** showed higher water and oil (n-hexadecane) contact angles and lower frictioncoefficient than the conventional anti-staining material **1a**. This is because **3** has more fluorine contents in a molecule and a higher degree of molecular mobility, due more to an ether bond than **1a**. Higher hydrophobic and oleophobic properties will give a better anti-staining performance. Moreover, a lower friction coefficient predicts that **3** will have both better stay-clean and easy-to-clean characteristics.
# The Recent Advancement in Unmanned Aerial Vehicle Tracking Antenna: A Review
## 3 Antenna Overview
### Log-Periodic Antenna
#### 3.7.2 Log-Periodic Dipole Array Antenna  
This antenna is always very handy in cases where there are wideband applications. It operates in a VHF range from 30 MHz to 300 MHz. The bandwidth of the antenna is usually greater than 10:1, and they are referred to as independent antenna. The features of this antenna, such as gain, input impedance, and radiation, changes periodically with respect  
Figure 8: Bowtie antenna [60].  
to the logarithm of the frequency space. This is referred to as a log-periodic array [70; 71]. Figure 9 demonstrates the structure of the log-periodic dipole array antenna.
# Compression Challenges in Large Scale Partial Differential Equation Solvers
## 5 Mass Storage
### Adjoint Solutions
#### 5.1.1 PDE-Constrained Optimization
Up to discretization error accuracy, lossy compression has no significant impact on the optimization progress. The adaptively chosen quantization tolerances for the state values are shown in Figure 8. The resulting compression factors when using TCUG as discussed in Section 4.2 together with delta encoding in time shown as well. In the first iteration, a user-prescribed tolerance was used. The small compression factors are on one hand due to the compression on adaptively refined grids, as discussed in Section 5.1.3, and on the other hand due to overestimation of the error in the worst case error estimates. The latter is apparent from the comparison with prescribed fixed quantization tolerances (see Figure 7). To increase the performance of the adaptive method, tighter, cheaply computable error estimates are required. Figure 7: Optimization progress of Broyden, Fletcher, Goldfarb, and Shanno (BFGS) for the monodomain example (9), (10), using different quantization tolerances for the state trajectory. No delta-encoding between timesteps was used. The horizontal line shows the approximate discretization error of the reduced gradient. See also [34].
## 8 Presentation of Results
### Presentation of Failure Prediction Model Results, Comparisons and Discussions
#### 8.1.1 Development of the Failure Prediction Model by Using Machine Learning  
Method 1: The compressor degradation state prediction method was used for the following algorithms: SVM, logistic regression, and Naive Bayes. Table 3 shows an example of discrete cluster 1, which shows the results of the degradation state prediction.  
Since the prediction rate obtained was low (did not exceed 30%), the method of fixing X was used.  
Method 2: After fixing X hours (with X = 0, 5, 24, 90, 100, and 500), the following learning algorithms were applied to predict the probability that the compressor will fail before these X hours that we fixed. Table 4 below shows the results found with X= 90, 24, and 0 h before the failure occurred.  
Even if we see that the optimal X was 90 h or about 4 days with a success rate for failures, we see that we have only 20% (because 41/(161 + 41) = 0.2). However, this method works very well to detect a failure when it has happened (X = 0 h before the occurrence of a failure).  
Method 3: As mentioned before, the objective is to predict the remaining lifetimes for the compressor; to solve this problem, we must first choose suitable estimators, then evaluate these estimators on our dataset, and finally compare the estimators and choose what performs the best. Choosing an estimator to solve a problem is not a simple task. The general objective is to minimize the prediction error; for this, and in order to efficiently predict our variable to be explained, time series and machine learning algorithms were developed, and we present two different types of forecasting techniques. The best-performing technique for the given case study was determined. Table 5 shows the results found when using the machine learning algorithms. However, these algorithms do not always predict the exact time of the outages, either before or after, and sometimes do not detect that there is a failure for certain types. This led to the use of time series for prediction.
## 4 Semantic Web
Linked data are, therefore, more of a concept or a set of guidelines, whereas RDFs and ontologies are the technologies, or implementations, used to achieve the goals of linked data. In this sense, linked data are similar to REST, which is not an enforced standard of API communication, but rather a set of guidelines that many software products adhere to strictly. The purpose of linked data, as envisioned by [PERSON], was to help end users navigating the semantic web as they would the regular web [11]; however, it is important to note that the semantic web and the regular web are not entirely similar. One of the key differences between the semantic web and the regular web is that the former is created, primarily, to service AIs and machines. This means that there are key differences in the data representation inside the semantic web and the regular web. For example, RDFs are the building blocks of the semantic web whereas in the regular web, resources are linked together by HTML, which is designed for human consumption. When implementing semantic web-based applications, Apache Jena does not appear to have a rival in the Java ecosystem. It is the most well-supported framework and the most complete; therefore, it is easier to highlight the features and limitations of Jena, as seen in Table 2, rather than comparing it directly with other frameworks. Apache Jena is a powerful Java framework for working with RDF data and ontologies. It provides extensive support for RDF and OWL, including parsing, querying, reasoning, and serialization. Jena could almost be considered the only choice for Java developers; however, it does have some quirks that are worth noting, such as limited support for RDF*, SHACL validation, and some advanced features of OWL.
## 2 Materials and Methods  
In this section, the potential of GNSS-R techniques to estimate land surface parameters such as surface Soil Moisture (SM) is experimentally studied using 2014-2017 global data from the UK TechDemoSat-1 (TDS-1) mission. The sensitivity to SM of different GNSS-R observables computed at L1 (1575.42 MHz) and at left-hand circular-polarization (LHCP) is computed, after quality-filtering the data (spacecraft not 'in eclipse' and the direct signal not present in the Delay Doppler Maps (DDM)), filtering the data for topography effects, correcting for background noise, antenna pattern gain, different distance propagation factors, vegetation attenuation, and taking into account the dependence with the incidence angle. The analysis is first conducted on a global scale using SMOS L3 products (SMOS nominal resolution, re-gridded at 25 km), then at regional scale using SMOS L4 products (1 km resolution), and finally using in situ sensors. The potential dependence with subsurface SM is also analyzed in the Brazilian \"semi-arid\" region using the LISFLOOD and MUSA models.
# Evaluation and Testing System for Automotive LiDAR Sensors
## 5 Results
### FoV Test
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Horizontal**} & \multicolumn{2}{c}{**Horizontal**} & \multicolumn{2}{c}{**Vertical**} & \multicolumn{1}{c}{**Vertical**} \\  & \multicolumn{2}{c}{**FoV (min)**} & \multicolumn{1}{c}{**FoV (max)**} & \multicolumn{1}{c}{**FoV (min)**} & \multicolumn{1}{c}{**FoV (max)**} \\ \hline \multirow{2}{*}{Test 1} & FoVSF & 0\({}^{\circ}\) & 60\({}^{\circ}\) & \(-\)10\({}^{\circ}\) & 15\({}^{\circ}\) \\  & Measured FoV & 0.03\({}^{\circ}\) & 59.89\({}^{\circ}\) & \(-\)1.95\({}^{\circ}\) & 14.89\({}^{\circ}\) \\ \multirow{2}{*}{Test 2} & FoVSF & 20\({}^{\circ}\) & 75\({}^{\circ}\) & \(-\)15\({}^{\circ}\) & 0\({}^{\circ}\) \\  & Measured FoV & 20.02\({}^{\circ}\) & 74.89\({}^{\circ}\) & \(-\)14.79\({}^{\circ}\) & \(-\)1.05\({}^{\circ}\) \\ \multirow{2}{*}{Test 3} & FoVSF & 0\({}^{\circ}\) & 135\({}^{\circ}\) & 0\({}^{\circ}\) & 17\({}^{\circ}\) \\  & Measured FoV & \(-\)1.08\({}^{\circ}\) & 134.70\({}^{\circ}\) & 0.04\({}^{\circ}\) & 16.92\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Test results for the FoV evaluation. Comparing the measured FoV values with the FoVSF parameters, makes it possible to observe the deviations of angles from the expected values and the readings from the RotGon. In the performed tests, these values ranged from 0.02\({}^{\circ}\) (Test 2, min. Horizontal FoV) to 0.30\({}^{\circ}\) (Test 3, max. Horizontal FoV). This could be for three main reasons: (1) the smallest angle the RotGon can read is 0.01\({}^{\circ}\); (2) the sensor is being evaluated from the receiver's perspective, which is only based on analyzing the received point cloud; and (3) because a CF is being applied to the target's region in the point cloud, the number of points that belong to the cluster usually varies, which is mainly related to the sensor's precision, accuracy, and resolution. In all the results, the proper operation of the evaluation and testing platform is apparent, with certain calculated angles having slight deviations from the desired values. It is important to mention that our measurements are performed from the sensor receiver's perspective, and are only based on the point cloud data provided by the sensor for use by other (high-level) applications within the perception system of the car. Therefore, we consider these deviations to not be critical at this order of magnitude; they can be used to validate the sensors' parameters being tested. When more accurate analysis is required, it is possible to submit the sensor to an end-of-line testing scenario, which is part of the laboratory responsible for testing sensors under development within other Bosch projects. However, end-of-line testing for the laser transmitter is beyond the scope of this article.
# Utilizing Sentinel-2 Satellite Imagery for LULC and NDVI Change Dynamics for Celephu, Bhutan
## 3 Results and Discussions
### Accuracy Assessment of LULC Maps  
The LULC raster files are imported into ArcGIS and 51 testing sample points (Figure 8) are overlayed. A TP occurs when a test sample representing a particular land cover class is correctly classified as that class in the LULC map. Conversely, FP results arise when a test sample representing a particular land cover class is incorrectly classified as another class in the LULC map. TP indicates the accuracy of the classification model (RF) in identifying a particular land cover class. The higher the number of TPs, the more accurate the classification is for that class.  
The confusion matrices (Table 2) of ROC analysis for the 2016 and 2023 image classification model by RF classifier are shown in Table 2. The actual classes represent the true positive (TP) and the predicted classes represent the false positive (FP). The point where TP and FP intersect in the confusion matrices provides pivotal information on the accuracy of the classification model. The classification model for 2016 and 2023 indicates a consistent TP result across both years, indicating stability in positive class predictions. The vegetation and barren land classes show a higher positive class prediction among the other two classes, least of all for the water classes.  
Figure 8: Testing data set.  
The ROC for image classification models is shown in Figure 9. The RF classification model resulted in 0.883 AUC for 2016 LULC mapping (Figure 9a) and 0.890 AUC for 2023 (Figure 9b) for Celephu. The similarity in AUC values between the two time periods (2016 and 2023) indicates the robustness and consistency of the RF model's performance. The model's ability to maintain a high level of accuracy across different periods suggests that it is reliable and can effectively handle temporal changes in the landscape.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{\(\mathbf{Actual\backslash}\)}} & \multicolumn{5}{c}{**2016**} & \multicolumn{5}{c}{**2023**} \\ \cline{2-9}  & **Vegetation** & **Water** & **Built-Up** & **Barren Land** & **Vegetation** & **Water** & **Built-Up** & **Barren Land** \\ \hline Vegetation & 22 & 3 & 4 & 2 & 20 & 2 & 5 & 1 \\ \hline Water & 2 & 18 & 1 & 1 & 1 & 15 & 2 & 0 \\ \hline Built-up & 4 & 1 & 19 & 2 & 3 & 1 & 18 & 1 \\ \hline Barren land & 1 & 0 & 2 & 22 & 2 & 0 & 3 & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Confusion matrices for ROC analysis for the 2016 and 2023 image classification models, respectively.  
Figure 9: Model accuracy assessment. (**a**) ROC for 2016 LULC, (**b**) ROC for 2023 LULC.
# A Short S-Equol Exposure Has a Long-Term Inhibitory Effect on Adipogenesis in Mouse 3T3-L1 Cells
## 3 Results
### S-Equol Inhibits Adipocyte Differentiation of Cells 3T3-L1  
To examine the effect of S-equol on adipocyte differentiation, confluent 3T3-L1 fibroblasts were induced to differentiation in DM-I containing 1, 3, and 10 \(\mu\)M of S-equol for three days and subsequently kept in S-equol free DM-II and MM, as described above. As expected, the size of control cells without S-equol progressively increased from day 5 of adipocyte differentiation; the shape became semi-rounded and intracellular lipid droplets were formed; notably, these morphological changes that are associated with an initial stage of adipocyte differentiation were more visible in the following days. These changes were even more pronounced in cells treated with 2 \(\mu\)M of rosigiltazone used as a positive control; on day 9, the cell monolayer appeared similar to mature adipose tissue (Figure 3). Treatment with 1 and 3 \(\mu\)M of S-equol did not significantly affect cell differentiation in  
Figure 2: Effect of S-equol on 3T3-L1 cell viability. 3T3-L1 fibroblasts were treated with S-equol for 24 (**A**) and 48 h (**B**), while differentiating 3T3-L1 were treated with S-equol for three days (**C**), and cell viability was determined by MTT assay. Data were obtained from three independent experiments in triplicate and expressed as mean \(\pm\) SD. Statistical analysis was performed by one-way ANOVA with the Bonferroni post hoc test. ***** _p_\(<\) 0.0001, ** _p_\(<\) 0.01, * _p_\(<\) 0.05 vs. control. Dotted line, 85% cell viability.  
comparison with control cells, as cells exhibited a similar increase in size, the same morphological changes as the formation of lipid droplets, notably from day 5. Interestingly, cells treated with 10 uM of S-equol did not present the changes in lipid droplet formation associated with adipocyte differentiation on day 5; and this inhibitory effect remained until the seventh day of culture. On day 9, cells seemed to recover from the 10 uM S-equol effects, their size slightly increased, and lipid droplets were formed (Figure 3). Similar observations were made in cells treated with 10 uM of estradiol used as an inhibitor of adipocyte differentiation.