esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Silicon Photonics Micro-Transceiver with Multimode Interface
### Optical Receiver Performance at 105 \({}^{\circ}\)C  
Figure 14 shows the optical receiver sensitivity profile. The responsivity of the integrated germanium photodetector is 0.5 A/W with a frequency response of 18 GHz. The receiver IC has a gain adjustment function for high-temperature operation, and the degradation in receiver sensitivity is negligible at high temperatures.  
Figure 14: Optical bit error rate vs. received optical power.  
Figure 13: Optical eye diagrams on all four channels at 25 and 105 \({}^{\circ}\)C.  
Table 1 shows that with IEEE 802.3 bm-compliant forward error correction (FEC), reception sensitivity for a BER of \(\geq\)5 \(\times\) 10\({}^{-5}\) can be achieved with received optical power \(>\)\(-\)8.2 dBm at an ambient temperature of 40 \({}^{\circ}\)C, increasing slightly to \(-\)7.8 dBm at an ambient temperature of 105 \({}^{\circ}\)C, while without FEC applied, a BER of \(\geq\)10\({}^{-12}\) can be achieved with a received optical power of \(-\)5.6 dBm at an ambient temperature of 40 \({}^{\circ}\)C, increasing slightly to \(-\)5.0 dBm at an ambient temperature of 105 \({}^{\circ}\)C.
# Low-Loss Active Grid Impedance Cancellation in Grid-Connected Inverters with LCL Filter
## 5 Experimental Validation
### Transient Behavior  
The transient behavior of the inverter with the proposed active compensator is investigated under the relatively weak grid condition (\(L_{g}=0.1\) pu). The power rating is suddenly programmed from half to full power and vice versa. The corresponding experimental results waveforms are shown in Figure 25. They indicate that the inverter with the proposed compensator operates well with the power transients.
# An Iris Image Super-Resolution Model Based on Swin Transformer and Generative Adversarial Network
## 3 Method  
In this research, we utilize a Generative Adversarial Network (GAN) framework to construct a specialized super-resolution model for the enhancement of an iris recognition system. The model comprises two main components: a generator and a discriminator. The generator is responsible for generating iris super-resolution images, while the discriminator aids in training the generator by providing feedback on the generated images' quality.  
Figure 1 illustrates the integration of the super-resolution model into the iris recognition system. Within the figure, SwinGIris (generator) represents the generator component of the trained iris super-resolution model. The workflow of the iris recognition system integrated with a super-resolution model can be roughly described as follows: Firstly, the acquired low-resolution iris images are input into the SwinGIris (generator). The images are then reconstructed into high-resolution images. Subsequently, a quality evaluation is conducted, and if the quality is deemed inadequate, iris image recapturing is required. Following a successful quality evaluation, the iris images will undergo normalization and feature extraction to obtain the iris code. Finally, a comparison with the data in the iris database will help obtain the recognition result. In the experimental section, we use VGG16, ResNet101 and DenseNet121 as backbone extraction networks, respectively.  
This section will provide a detailed overview of the network model's architecture and its individual components.
# Approach-Based Analysis on Wireless Power Transmission for Bio-Implantable Devices
###### Abstract  
The wireless power transmission (WPT) is a systematic technology improve many constraints affecting implantable devices. Many methods have been introduced over the years for WPT. In this article, based on different approaches, we discuss and analyze philosophically the recent existing methodologies and techniques for efficient WPT in implantable devices. For each recent powering method or approach, the working principle and their outcomes are mapped. The performance, efficiency, operating frequency and stability of the systems have been highlighted and listed.  
power transmission efficiency (PTE); inductive power transmission (IPT); rectenna; impedance matching; implantable devices +
Footnote †: journal: Applied Sciences  
skin or any other tissue of the body. Apart from this, few implants are produced from plastics, metals or ceramics, etc., Implants can be used up to the point that the original organ gets its normal functionality back, while the permanent placement of implants is also possible in case of complete damage or failure of a particular organ. Some major challenges of having implanted medical devices are giving continuous power to them and tracking their functions. Power levels vary for different implantable devices as per their application. Normally, a pacemaker needs power of about 1 milliwatt maximum, whereas a retinal prosthetics needs around 40 mW of power and so on.  
Hence, an efficient WPT system is required for implantable devices to avoid such interruption during the function of the device. The basic WPT model is illustrated in Figure 1, where the power link is established by magnetic coupling of coils and so that power is transferred to the load. Even though many wireless power transfer systems are found using various recent techniques, advancements and improvements in terms of efficiency, distance coverage and stability are still required.
# Merging-Squeeze-Excitation Feature Fusion for Human Activity Recognition Using Wearable Sensors
## 5 Extensions of Merging-Squeeze-Excitation Feature Fusion
### Deep MSE Feature Fusion  
Instead of using only one-level MSE feature fusion to combine and recalibrate the feature maps \(\mathbf{A}^{(n)}\) as shown in Figure 1, we can stack a series of MSE feature fusion blocks to create deep MSE feature fusion, where feature maps are merged and weighted multiple times. The structure of deep MSE feature fusion is shown in Figure 4a, where \(D\) MSE feature fusion blocks are connected in series. The \(d\)-th block as shown in Figure 4b receives the channel-weighted feature maps \(\tilde{\mathbf{P}}^{(d-1,n)}\), for \(n=1,2,\ldots,N\), and the channel-weighted and merged feature maps \(\tilde{\mathbf{Y}}^{deep,(d-1)}\) from the previous block to create the new channel-weighted feature maps \(\tilde{\mathbf{P}}^{(d,n)}\) and the new channel-weighted and merged feature maps \(\tilde{\mathbf{Y}}^{mse,(d)}\). Note that \(\tilde{\mathbf{P}}^{(0,n)}\) and \(\tilde{\mathbf{Y}}^{deep,(0)}\) are equal to \(\mathbf{A}^{(n)}\) and \(\mathbf{B}\) (defined in (7)), respectively. Similar to Section 4.2.2, we have \(\tilde{\mathbf{P}}^{(d,n)}=\mathbb{F}_{Seg}\big{(}\tilde{\mathbf{P}}^{(d-1,n) },\tilde{\mathbf{Y}}^{deep,(d-1)}\big{)}\) and \(\tilde{\mathbf{Y}}^{mse,(d)}=\mathbb{F}_{Merge}\big{(}\tilde{\mathbf{P}}^{(d,1 )},\tilde{\mathbf{P}}^{(d,2)},\ldots,\tilde{\mathbf{P}}^{(d,N)}\big{)}\). Thereafter, the new merged feature maps \(\tilde{\mathbf{Y}}^{deep,(d)}\) will be obtained from  
\[\tilde{\mathbf{Y}}^{deep,(d)}=\tilde{\mathbf{Y}}^{deep,(d-1)}+\tilde{\mathbf{ Y}}^{mse,(d)}, \tag{21}\]  
Figure 3: MSE feature fusion architecture with global channel attention. We compute an additional set of channel-weighted feature maps \(\mathbf{R}\) which is obtained from the pre-merged feature maps \(\mathbf{B}\). The output feature maps \(\mathbf{Y}^{sca}\) contain both \(\mathbf{Y}^{mse}\) (where feature maps are calibrated and, then, merged) and \(\mathbf{R}\) (where feature maps are merged and, then, calibrated).  
where a skip connection is used to keep the deep MSE feature fusion stable. The feature maps \(\hat{\mathbf{Y}}^{deep,(D)}\) of the last block will be used in the classification process.  
Figure 4: Deep MSE feature fusion. We implement a series of \(D\) MSE feature fusion blocks such that the feature maps \(\mathbf{A}^{(1)}\), \(\mathbf{A}^{(2)}\), \(\ldots\), \(\mathbf{A}^{(N)}\) are calibrated and merged several times as shown in (**a**) deep MSE feature fusion architecture, where the structure of the \(d\)-th MSE feature fusion block is shown in (**b**). The output feature maps \(\hat{\mathbf{Y}}^{deep,(D)}\) are used in the classification process.
# A Secure Link-Layer Connectivity Platform for Multi-Site NFV Services
## 4 Implementation and Validation
### Performance Evaluation
#### 4.2.1 Practical Experiments and Results
If the security mechanisms of IPsec are disabled, increasing the number of virtual CPUs on the L2S VNFs does not result in a positive impact on the achievable throughput. This indicates that VXLAN encapsulation and decapsulation processes cannot benefit from the parallelization offered by multiple virtual CPUs (as a matter of fact, the overhead of coordination among virtual CPUs translates into a slightly downward trend in throughput). When IPsec security mechanisms are enabled, we can observe an increase of performance with the number of virtual CPUs allocated to L2S VNFs. This is most noticeable with an MTU value of 1400 bytes on the L2S VLANs, as this value avoids the execution of fragmentation and reassembly procedures by L2S VNFs. In this case, using two CPUs increases the maximum average throughput to 902.55 Mb/s, i.e., an increment of approximately 46.4% with respect to the use of a single virtual CPU. As the number of virtual CPUs increases, the achievable throughput converges to the value that can be obtained when IPsec mechanisms are disabled (with 4 virtual CPUs, the former is approximately 91.3% of the latter).
# Design of a Compact Ultra-Wideband Microstrip Bandpass Filter
## 5 Conclusions  
A multilayer UWB bandpass filter based on MPCB is proposed in this paper, which has a simple design structure, and is easy to integrate with other electronic devices. The UWB bandpass filter has a 3 dB fractional bandwidth of 40.33%, an IL of 0.58 dB at the center frequency of 12.795 GHz, an in-band RL that is better than 18 dB and a flat group delay of less than 0.1 ns in the passband. The results of measurement and simulation are in good agreement. This filter has a broad application prospect in the fields of radio positioning, satellites and radar systems. In the future, more resonance points or transmission zeros will be introduced to suppress out of band harmonics, achieve better stopband characteristics and improve the utilization of dielectric substrates.  
Conceptualization, C.L.; methodology, C.L. and J.-X.C.; software, C.L., J.-X.C., M.-N.W. and J.-M.H.; validation, C.L.; formal analysis, C.L., Z.-H.M. and J.-X.C.; investigation, C.L., M.-N.W. and J.-M.H.; writing--original draft, C.L.; writing--review & editing, C.L. and Z.-H.M.; supervision, Z.-H.M.; project administration, Z.-H.M.; funding acquisition, Z.-H.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Fujian Natural Science Foundation Project, grant number 202201823.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Ref.** & **Technology** & **3 dB FBW (\%)** & \(\mathbf{f_{0}}\) (GHz) & \(\mathbf{\varepsilon}_{r}\) & **IL (dB)** & **RL (dB)** & **TZs** & **Layer** & **Size (mm\({}^{\circ}\))** \\ \hline \([2]\) & HMSiw & 110.1 & 6.85 & 3.55 & \(<\)1.6 & \(>\)12 & 4 & 1 & 16 \(\times\) 14.5 \(\times\) 0.508 \\ \([7]\) & MMR & 62.6 & 4.585 & 3.55 & \(<\)2 & \(>\)15 & 3 & 1 & 16.88 \(\times\) 16.68 \(\times\) 0.787 \\ \([13]\) & LCP & 109.5 & 6.85 & 3.15 & 0.4 & \(>\)10 & 7 & 3 & 16.75 \(\times\) 19 \(\times\) 0.8 \\ \([17]\) & Hybrid & 10.3 & 3.128 & - & 2.2 & \(>\)11 & 2 & 10 & 0.6 \(\times\) 0.4 \(\times\) 0.506 \\ \([20]\) & MPCB & 58 & 4.2 & 3/3.66 & 0.95 & \(>\)13 & 0 & 3 & 45.05 \(\times\) 18.02 \(\times\) 2003 \\ \([21]\) & MPCB & 35.5 & 5.87 & 3.55 & 1.2 & \(>\)15 & 3 & 2 & 50.3 \(\times\) 6.6 \(\times\) 0.813 \\ \([29]\) & MPCB & 10 & 10.71 & 2.2 & 1.57 & \(>\)15 & 3 & 2 & 23.5 \(\times\) 12.6 \(\times\) 1.016 \\ This work & MPCB & 40.33 & 12.795 & 3.66 & 0.58 & \(>\)18 & 3 & 3 & 20.8 \(\times\) 20 \(\times\) 1.34 \\ \hline \end{tabular}
\end{table}
Table 2: Comparison of performance with other designed filters.  
Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available because the data has not been uploaded to publicly archived datasets for the time being.  
The authors declare no conflict of interest.
# Machine Learning-Powered Rainfall-Based Landslide Predictions in Hong Kong--An Exploratory Study
## 3 The Pilot Study--ML-Powered Natural Terrain Landslide Susceptibility Analysis
### Feature Selection  
Feature selection is the process of reducing the dimensionality of input variables and creating summary measures to encapsulate the information in the entire dataset. Domain knowledge is used in the process to extract the characteristics and attributes from the raw data. [PERSON] (2018) [12] summarized the conditioning factors or features adopted in landslide susceptibility models, which were published in 565 articles from 1983 to 2016. In total, about 600 features were used, with 2-22 input variables adopted in a single model. While the study remarked that the use of morphology related features is particularly effective in predicting the spatial distribution of landslides, it also pointed out that some of the adopted features lack a physical correlation with landslide occurrence (e.g., aspect and elevation). The study recommended that more time should be spent on understanding the known or inferred role of the features in determining landslide susceptibility. In addition, redundant or irrelevant factors may create noise, decreasing the overall predictive capability of the models [15]. It suggested that in the process of seeking the proper parameters and threshold of each feature, conventional feature engineering involves a substantial amount of prior knowledge that should be carried out.  
In view of the above, a new feature selection framework which ensures the quality, and the statistical and physical relevance of the features identified for inclusion in the ML models was proposed in this study, as deliberated in the following.
## 4 Conclusions  
The form of sulfur used for fertilization had a significant impact on the parameters investigated in this study. For all tested parameters, the lowest results were recorded for the control sample without sulfur fertilization. We also noted that the form of sulfur applied affected individual parameters differently. Sulfur fertilization improved the nutritional value of cabbage. Higher levels of vitamin C, phenolics, and chlorophyll a and b were detected in cabbage treated with potassium sulfate, while a higher concentration of carotenoids was seen when elementary sulfur and ammonium sulfate were used. Elementary sulfur had a small effect on the antioxidant activity in comparison with potassium sulfate and ammonium sulfate. The highest phytic, citric, malic, and fumaric acid contents were found in crops treated with ammonium sulfate. Sulfur fertilization had very little impact on the total amino acid levels in the cabbage heads.  
Irrespective of the method of sulfur fertilization, storage resulted in a decrease in vitamin C, chlorophyll a and b, and carotenoid contents, while polyphenol and amino acid contents increased, along with antioxidant activity.  
After storage, the organic acid content in most cases decreased, and only phytic acid content increased for all plots in this study. By contrast, storage of cabbage grown using potassium sulfate as the fertilizer increased the content of all four organic acids.  
Correlation analysis demonstrated that the correlation of bioactive compounds in white cabbage was higher in relation to the antioxidant activity in the ABTS assay than that in the DPPH assay.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Vitamins C** & **Phenolics** & **Chlorophyll a + b** & **Carotenoids** & **Amino Acid** \\ \hline ABTS & 0.9590 & 0.4892 & 0.6158 & 0.5871 & 0.8649 \\ DPPH & 0.9626 & 0.3957 & 0.4527 & 0.4304 & 0.7787 \\ \hline \multicolumn{5}{c}{After storage} \\ \hline ABTS & \(-\)0.5392 & 0.6588 & 0.8281 & 0.6008 & 0.9075 \\ DPPH & \(-\)0.1408 & 0.3385 & 0.5743 & 0.3798 & 0.8382 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Correlation of antioxidant activity and bioactive compound content in fresh and stored cabbage.  
**Author Contributions:** Conceptualization, A.B.; methodology, A.N.-O.; software, A.B. and A.K.; validation, A.K. and A.N.-O.; formal analysis, A.N.-O. and A.K.; investigation, A.B., A.N.-O. and A.K.; resources, A.K.; data curation, A.K. and A.N.-O.; writing--original draft preparation, A.N.-O.; writing--review and visualization, A.N.-O. and A.B.; supervision, A.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
This publication was the result of the activity of the Plants4 food research group.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Miniaturized Near-Infrared Analyzer for Quantitative Detection of Trace Water in Ethylene Glycol
## 2 Materials and Methods
### Near-Infrared Modeling Methods
#### 2.4.2 Model Establishment and Analysis  
In this study, we first established models using partial least squares regression (PLSR), principal component regression (PCR), and support vector machine regression (SVMR) based on raw data without preprocessing, and then compared their performance. By evaluating the correlation coefficient for the training set (Rc), the root mean square error of calibration (RMSEC), the correlation coefficient for the validation set (Rp), and the root mean square error of prediction (RMSEP), the optimal algorithm (PLSR) was selected. Subsequently, the model's performance was further optimized by integrating various spectral preprocessing methods (e.g., smoothing, first derivative, etc.), ultimately constructing a high-precision predictive model. Additionally, the Prediction Residual Error Sum of Squares (PRESS) was introduced as a key evaluation metric to quantify the cumulative prediction errors across validation samples, providing a comprehensive measure of the model's generalization ability. The results indicate that the closer the R' value is to 1 and the lower the root mean square error, the stronger the model's predictive capability, while the PRESS value offers supplementary validation of the model's robustness from a statistical perspective [33].  
All multivariate calibration models (PLSR, PCR, and SVMR) were developed using Python 3.8 programming language within the PyCharm Community Edition 2020.3.5 x64 integrated development environment (JetBrains, Prague, Czech Republic). The scikit-learn library (version 1.0.2) was employed for algorithm implementation, including spectral  
Figure 1: Schematic diagram of the experimental test setup.  
preprocessing (Savitzky-Golay smoothing, first-derivative transformations) and model training. Interval Partial Least Squares (iPLS) analysis was conducted using custom scripts adapted from the pyChemometrics package (version 0.1.5). Statistical calculations for the Limit of Detection (LOD) utilized SciPy's statistical module (version 1.7.3), ensuring rigorous validation of results. Data visualization and spectral analysis were performed using Matplotlib (version 3.5.1) and pandas (version 1.3.5).
Pathogens_**2015**, \(4\), 573-589; doi:10.3390/pathogens4030573  
_Article_  
**Coordination of _Candida albicans_ Invasion and Infection  
Functions by Phosphoglycerol Phosphatase Rhr2**  
**[PERSON] 1,\({}^{\dagger}\), [PERSON] 2, [PERSON] 1, [PERSON] 3, [PERSON] 4,5, [PERSON] 6 and [PERSON] 7,**  
Footnote 1: The _Candida albicans RHR2_ gene, which specifies a glycerol biosynthetic enzyme, is required for biofilm formation _in vitro_ and _in vivo_. Prior studies indicate that _RHR2_ is ultimately required for expression of adhesin genes, such as _ALS1_. In fact, _RHR2_ is unnecessary for biofilm formation when _ALS1_ is overexpressed from an _RHR2_-independentpromoter. Here, we describe two additional biological processes that depend upon _RHR2_: invasion into an abiotic substrate and pathogenicity in an abdominal infection model. We report here that abiotic substrate invasion occurs concomitantly with biofilm formation, and a screen of transcription factor mutants indicates that biofilm and hyphal formation ability correlates with invasion ability. However, analysis presented here of the _rhr2 Al/l_ mutant separates biofilm formation and invasion. We found that an _rhr2 Al/l_ mutant forms a biofilm upon overexpression of the adhesin gene _ALS1_ or the transcription factor genes _BRG1_ or _UME6_. However, the biofilm-forming strains do not invade the substrate. These results indicate that _RHR2_ has an adhesin-independent role in substrate invasion, and mathematical modeling argues that _RHR2_ is required to generate turgor. Previous studies have shown that abdominal infection by _C. albicans_ has two aspects: infection of abdominal organs and persistence in abscesses. We report here that an _rhr2 Al/l_ mutant is defective in both of these infection phenotypes. We find here that overexpression of _ALS1_ in the mutant restores infection of organs, but does not improve persistence in abscesses. Therefore, _RHR2_ has an adhesin-independent role in abdominal infection, just as it does in substrate invasion. This report suggests that _RHR2_, through glycerol synthesis, coordinates adherence with host- or substrate-interaction activities that enable proliferation of the _C. albicans_ population.  
**Keywords:**_Candida albicans_; adherence; invasion; biofilm; intra-abdominal candidiasis; glycerol; turgor; infection; adhesin
## 3 Results and Discussion
### Effect of the Impeller Clearance Evaluated from PEPT Experiments
Thus, from Figure 9 it can be inferred that for the higher value of clearance once the tracer was able to access to the bottom layers of media it was trapped for a long time before it could be ejected from this region. This is demonstrated in Figure 10 by plotting the axial tracer position (Z) against the time of the PEPT experiment for the two impeller clearance values. Figure 10 highlights that the clearance of 19 mm resulted in roughly the same frequency of tracer visits to the bottom of the mill, but every tracer visit was noticeably longer, up to three minutes, compared to the lower impeller clearance. Therefore, considering that the same bead loading and recording time was used for these two experiments, the data so far presented suggest that the use of the minimum clearance will reduce the size of the region at low velocity located at the attritor bottom. Figure 10: Axial tracer position (Z) as function of the time of the PEPT experiments for two impeller clearance values: (**a**) 12.7 mm and (**b**) 19 mm. The impeller position is represented by the red straight line.