esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Summary and Conclusions  
The paper discussed the absorptive and dispersive characteristics of fog and their effects on the accuracy of a radar operating in the millimeter wave regime. A theoretical analysis of the attenuation and group delay emerged due to the suspension of water droplets in the atmosphere, occurring in foggy weather conditions. We derive the relations between the optical visibility in foggy conditions and the dielectric properties of the atmospheric medium, as well as its complex refractivity.  
The effects of the fog on radar accuracy are demonstrated by generating fog artificially with a fog machine. The resulted heavy fog produced in a closed corridor enables an experimental realization of the theory even in a relatively short distance. The dimensions of the generated fog droplets were small enough to keep the millimeter wave scattering well within the Rayleigh regime and neutralize variations in the environmental conditions, such as temperature and pressure that may affect droplet sizes. Small droplet dimensions enabled us to distribute the fog quite uniformly along the corridor, and bring the visibility to be low enough to demonstrate absorption and dispersion effects caused by the suspended small droplets.  
The measurements were done using high resolution FMCW radar operating at 330 GHz. The high bandwidth of the radar and its extended frequency sweep, allow us to reveal the effects quantitatively. The experimental measurements showed an agreement with the calculated results predicted by the theory.  
Conceptualization, Y.G.; Data curation, A.E.; Formal analysis, Y.G., G.A.P. and Y.G.; Investigation, Y.G. and A.E.; Methodology, Y.G. and G.A.P.; Supervision, G.A.P. and Y.G.; Validation, A.E.; Writing--original draft, Y.G. and A.E.; Writing--review & editing, G.A.P. and Y.G.  
We would like to thank the laboratory engineers, [PERSON], and [PERSON] for their help in performing the experiments.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Parameter Name**} & \multirow{2}{*}{**Parameter Symbol**} & \multirow{2}{*}{**Units**} & \multicolumn{3}{c}{**Model**} & \multicolumn{2}{c}{**Experiment**} \\ \cline{3-6}  & & & **No Fog** & **Fog** & **Change** & **Difference** \\ \hline Visibility & _Vis_ & (m) & \(\infty\) & 0.5 & & \\ Water content & \(W_{0}\) & (g/cm\({}^{3}\)) & 0 & 30 & & \\ Attenuation & \(20\log(e)\cdot\alpha(f)\) & (dB) & 0.35 & 2.1 & 1.75 & 1.5 \\ Intermediate frequency & \(f_{IF}\) & (Hz) & 130,622 & 130,631 & 9 & 9 \\ Group delay & \(\tau_{d}\) & (ps) & 62,752 & 62,756 & 4 & 4.3 \\ Incremental group delay & \(\Delta\tau_{d}\) & (ps) & 2710 & 2714 & 4 & 4.3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation and experimental results for frequency of 320 GHz at distance \(d=18.8\) m.
###### Abstract  
This work aims to provide the hardware (HW) design of the optoelectronics interfaces for a visible-light communication (VLC) system that can be employed for several use cases. Potential applications include the transmission of ultra-high-definition (UHD) streaming video through existing reading lamps installed in passenger vans. In this use case, visible light is employed for the downlink, while infrared light is used for the uplink channel, acting as a remote controller. Two primary components -a Light Fidelity (LiFi) router and a USB dongle--were designed and implemented. The 'LiFi Router', handling the downlink channel, comprises components such as a visible Light-Emitting Diode (LED) and an infrared receiver. Operating at a supply voltage of 12 V and consuming current at 920 mA, it is compatible with standard voltage buses found in transport vehicles. The 'USB dongle', responsible for the uplink, incorporates an infrared LED and a receiver optimized for visible light. The USB dongle works at a supply voltage of 5 V and shows a current consumption of 1.12 A, making it well suited for direct connection to a universal serial bus (USB) port. The bandwidth achieved for the downlink is 11.66 MHz, while the uplink's bandwidth is 12.27 MHz. A system competent at streaming UHD video with the feature of being single-input multiple-output (SIMO) was successfully implemented via the custom hardware design of the optical transceivers and optoelectronics interfaces. To ensure the system's correct performance at a distance of 110 cm, the minimum signal-to-noise ratio (SNR\({}_{\text{min}}\)) for both optical links was maintained at 10.74 dB. We conducted a proof-of-concept test of the VLC system in a passenger van and verified its optimal operation, effectively illustrating its performance in a real operating environment. Exemplifying potential implementations possible with the hardware system designed in this work, a bit rate of 15.2 Mbps was reached with On-Off Keying (OOK), and 11.25 Mbps was obtained with Quadrature Phase Shift Keying (QPSK) using Orthogonal Frequency-Division Multiplexing (OFDM) obtaining a bit-error rate (BER) of 3.3259 \(\times\) 10\({}^{-5}\) in a passenger van at a distance of 72.5 cm between the LiFi router and the USB dongle. As a final addition, a solar panel was installed on the passenger van's root to power the user's laptop and the USB dongle via a power bank battery. It took 13.4 h to charge the battery, yielding a battery life of 22.3 h. This characteristic renders the user's side of the system entirely self-powered.  
analog-to-digital interface; digital-to-analog interface; energy harvesting; light fidelity; optical wireless communications; self-powered system; visible light communication +
Footnote †: journal: _Sensors_**2024**, 24, 5829. [[https://doi.org/10.3390/s24175829](https://doi.org/10.3390/s24175829)]([https://doi.org/10.3390/s24175829](https://doi.org/10.3390/s24175829))
## 5 Experiments
### Results and Discussion
#### 5.3.5 Results of the Different Parameters  
To evaluate the performance of the proposed method and find the best experimental results, we conducted a series of experiments on the CEDAR dataset, testing different parameter conditions. We tested different batch sizes and plotted the experimental results in Figure 7. The experimental results show that the accuracy does not strictly follow a linear growth relationship and may decrease as the batch size increases. Based on our experimental results, we found that using a batch size of 32 achieved the best performance for our proposed method on the CEDAR dataset, with an accuracy rate of 96.21%. This result provides important foundational support for our subsequent experiments.
# Improvement of the Transglycosylation Efficiency of a
## 3 Results and Discussion
### Significance of the Donor Substrate
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{3}{c}{**Reaction Conditions**} & \multicolumn{3}{c}{**LNT Formation**} & **Total LNT Isomer (TG) Formation** \\ \hline
**pNP-LNB** & **Lactose** & **A/D** & & & \\
**[mM]** & **[mM]** & **Ratio** & & & \\ \hline
2.5 & 200 & 80 & 107 \(\pm\) 14 e (2 h) & 4\% b & 225 \(\pm\) 26 e (2 h) & 9\% c \\
2.5 & 400 & 160 & 188 \(\pm\) 22 e (2 h) & 8\% a & 396 \(\pm\) 25 d (2 h) & 16\% a \\
5 & 200 & 40 & 247 \(\pm\) 11 cd (2 h) & 5\% b & 539 \(\pm\) 25 c (2 h) & 11\% b \\
5 & 400 & 80 & 342 \(\pm\) 12 bc (2 h) & 7\% a & 765 \(\pm\) 28 b (2 h) & 15\% a \\
10 & 200 & 20 & 389 \(\pm\) 41 b (24 h) & 4\% b & 759 \(\pm\) 23 b (2 h) & 8\% c \\
10 & 400 & 40 & 746 \(\pm\) 81 a (24 h) & 7\% a & 1224 \(\pm\) 65 a (2 h) & 12\% b \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Optimization of substrate concentrations for the transglycosylation reaction with LnbB W394H using pNP-LNB as donor substrate (D) and lactose as acceptor substrate (A) at pH 4.5 and 30 \({}^{\circ}\)C with 1 mM of enzyme. Product concentrations and molar yield (calculated on donor substrate) of LNT and of LNT plus isomers (total transglycosylation products; TG) are given, with the time at which the maximum yield is reached given in brackets (monitored over 24 h). Superscript letters (a–e) indicate statistically significant difference between the values within the column (\(p<0.05\)).
## 3 Materials and Methods
### Simulation Methodology
* Recommendation ITU-R P.452 \"Prediction procedure for the evaluation of interference between stations on the surface of the Earth at frequencies above about 0.1 GHz\" with 20% percentage of time, 452 was used to estimate interference level to the MMS receivers [23]. * Recommendation ITU-R P.2108 \"Prediction of clutter loss\" with 20% of location, the clutter was applied to all 5G interfering BS [24];
* Recommendation ITU-R P.2109 \"Building entry loss\" with 50% traditional and 50% thermally efficient buildings, this model was applied for the indoor 5G interfering UEs [25]. In addition, for both scenarios, terrain data were used for every interfering link, and Figure 9 shows an example of terrain losses from 1 BS located in Ussuriysk to the maritime station. For AMS, no terrain is required since transmission mode from an airborne station to a region before its radio horizon is the line-of-sight mode, and thus the terrain could barely affect the propagation between the 5G station and AMS and therefore was not considered. Figure 8: Typical 5G NR urban network topology.
# Portable Sensor System for Registration, Processing and Mathematical Analysis of PPG Signals
## 2 Portable Sensor System for Registration, Processing and Analysis of PPG Signals
### Preprocessing and Mathematical Methods for PRV Analysis
#### 2.3.4 Data Collection  
The data with which the study was conducted in this article were recorded while using the experimental photoplethysmographic device created. The investigated data are two types:  
* data with which the device is tested; and,
* data to perform mathematical analysis of two groups of subjects: healthy controls and patients with arrhythmia.  
The data that test the device are for a healthy subject (male, 51 years old). Four signals of 30 min. duration are recorded simultaneously: ECG signal; PPG signal obtained from a sensor placed on the left ear auric; A PPG signal obtained from a sensor placed on the middle finger of the left hand and a PPG signal obtained by placing the left hand index finger on the Discrete HR/SPO2 sensor that is built into the device. After registration of the ECG and PPG signals, specialized software determines the RR time intervals of the ECG signal and PP intervals of the PPG signal. The mean squared error (MSE) is calculated while using the following formula to demonstrate the coincidence of the intervals between the two types of signals:  
\[\text{MSE}=\sqrt{\sum_{i=1}^{N}\left(x_{i}-y_{i}\right)^{2}} \tag{1}\]  
The variables x and y are denoted the PP and RR intervals, respectively; N is the number of intervals. When the MSE tends to zero, it indicates that the intervals of the two type investigated signals (ECG and PPG) are very close, ie they are identical.  
The data are used to perform the mathematical analysis are following: 42 subjects with arrhythmia (volunteers diagnosed by a cardiologist) and 40 healthy individuals (volunteers who are not diagnosed with cardiac disease). Recordings are for a duration of 120 min. The studies were made while using an external sensor in the form of a pinch placed on the middle finger of the left hand of the test subject. The experiment involves a member of the technician team who checks the functioning of the created photoplethysmographic device and the reliable operation of the software program in the device.  
The participating volunteers are familiarized with the instructions for recording the photoplethysmographic signals, after which each participant is made three consecutive registrations of PPG signals. The purpose of the three measurements is to verify the correctness of the data. Artifacts or other events are detected if the validation of the data is successful and no extraordinary noises, and they are then transmitted to the software module of the developed web application for preprocessing to obtain PP interval series, to which mathematical methods of analysis are applied. The signals registered with the developed device have a sampling rate of 60 Hz and a 22-bit resolution, thus avoiding the possibility of inaccuracies resulting from the distortion of the input signal.
# Dominant Partitioning of Discontinuities of Rock Masses Based on DBSCAN Algorithm
## 2 Methods
### Fuzzy C-Mean Clustering Algorithm  
Fuzzy C-means clustering algorithm is a classical and widely used fuzzy clustering method in various fields. It was first proposed by [PERSON] in his doctoral dissertation.  
A given discontinuous surface orientation data set, \(X=\{X_{1},X_{2},\) or \(X_{n}\}\), can be divided into \(c\) classes, that is, expressed as \(C=(C_{1},\)\(C_{2},\)..., \(C_{c})\), and the division matrix \(U(X)\) can be obtained, expressed as \(U(X)=[u_{ij}]_{c\times n}(i=1,\)...\(1c,\)\(j=1,\)...\(,\)\(n)\), where \(u_{ij}\) is the  
Figure 1: Spatial model of discontinuity occurrence (adapted with permission from Ref. [11], [PERSON], 1976).  
membership degree of the sample \(x_{j}\) to the \(C_{i}\) class. The division result needs to meet the following conditions:  
\[\left\{\begin{aligned} U_{i=1}^{c}C_{i}=X\\ C_{i}\cap C_{j}=\varnothing,i,j=1,\cdots,c;i\
eq j\\ C_{i}\
eq\varnothing\qquad i=1,\cdots,c\end{aligned}\right. \tag{4}\]  
This method can be used to divide the number of dominant groups of rock mass discontinuities via the following steps. The algorithm flow chart is shown in Figure 2:  
Figure 2: Flow chart of the fuzzy C-means clustering algorithm.  
1. Initialize the basic parameters of fuzzy C-means clustering algorithm to determine the number of cluster centers \(c\) (\(2\leq c\leq n\)).
2. Calculate the membership \(u_{ij}\) and the initial clustering center \(y_{i}\) of each cluster according to the results of the initial division. \[u_{ij}=1/\sum_{i=1}^{c}\left[d\left(x_{j},y_{i}\right)/d\left(x_{j},y_{i}\right) \right]^{\frac{1}{m-1}}\] (5) \[y_{i}=\sum_{j=1}^{n}\left(u_{ij}\right)^{m}x_{j}/\sum_{j=1}^{n}\left(u_{ij} \right)^{m}\] (6) where, \(u_{ij}\) is the membership degree of the \(j\)th discontinuity belonging to the \(i\)th subset \(C_{i}\), \(m\) is the fuzzy weighted index and the general value is 2. \(d(x_{i},y_{i})\) represents the distance from the \(j\)th discontinuity to the cluster center of the \(i\)th subset.
3. Compute the distance from each discontinuity to each cluster center, then reclassify according to the new distance. That is, each discontinuity is divided with the nearest group.
4. Solve and update the membership \(u_{ij}\) and clustering center \(y_{i}\) using Equations (5) and (6).
5. Calculate the objective function using: \[J(U,Y)=\sum_{i=1}^{c}\sum_{j=1}^{n}\left(u_{ij}\right)^{m}d\left(x_{j},y_{i}\right)\] (7)
6. Repeat steps (3), (4) and (5) to minimize the objective function.
7. The iteration process will end when no discontinuity can be reallocated to different clusters.
# Open DGML: Intrusion Detection Based on Open-Domain Generation Meta-Learning
## 1 Introduction  
Network security has become an increasingly important issue in recent years due to its critical role in various fields, including the economy, military, healthcare, and more. Establishing effective defenses against various types of network attacks and ensuring the security of network devices and information has become a highly important issue that requires careful consideration [1].  
As globalization and multipolar development continue, the flames of great power games have extended to the cyberspace. The scale and frequency of attacks against governments, industry chains, and other entities have increased year by year [2; 3]. Additionally, the COVID-19 pandemic has further catalyzed the urgency of network security issues. Intrusion detection is considered one of the mandatory defense lines to protect critical networks from increasing invasive activities [4].  
However, as new technologies such as 5G, industrial Internet, and cloud platform technologies rapidly develop, network intrusion forms and methods have also evolved. Traditional detection methods are inadequate to adapt to the new network intrusion scenarios [5; 6; 7]. Therefore, new approaches are needed to enhance intrusion detection and improve network security in the face of evolving threats.  
This paper discusses the use of meta-learning for intrusion detection in open domains, focusing on open-domain issues, network traffic processing, and meta-learning techniques. Open domain problems have three main challenges: differences in label sets between domains, differences in distribution between source domains, and the possibility of entirely new categories in the target domain. The research aims to develop a model with strong generalization performance on source domains in open domains and apply it to an unknown target domain. Meta-learning algorithms can solve the problem of insufficientdata samples, and require only a small amount of samples to achieve traffic identification and classification. Feature extraction from network traffic is necessary to reduce data redundancy and dimensionality and lower computational costs. The research results are expected to improve network security, prevent network attacks and intrusions, protect network devices and information, and support national security, economic development, and social stability.  
The contributions of this research are summarized as follows.  
* Introducing the open domain scenario, which allows the model to achieve generalization on an unknown target domain with only source domain training, addresses the limitations of traditional intrusion detection models and aligns more closely with real-world intrusion scenarios.
* Designing the Open DGML framework, which integrates flow image technology, data augmentation, and meta-learning algorithms to improve the performance of intrusion detection in open domains.
* Proposing the flow image technology and Mul-Mixup technique, which address the problem of insufficient sample size and improve the model's generalization performance.
## 5 Result and Discussion
### Receiver Operating Characteristic  
The Receiver Operating Characteristic (ROC) curve shows the performance of the binary classifier for various thresholds. Figure 7 shows the corresponding ROC curves when using ConvNet, DocClass_ConvNet_Mod, and RepSTDoc_ConvNet. ConvNet outperformed the other models in the lower-left corner. However, in the section where the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Avg. Epoch** & **Avg. Time (s)** & **Avg. Time per Epoch (s)** \\ \hline ConvNet & 405 & 387 & 0.912 \\ DocClass\_ConvNet\_Mod & 398 & 332 & 0.849 \\ RepSTDoc\_ConvNet & 687 & 903 & 1.313 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The comparison of the time.  
Figure 5: The effect of batch size.  
Figure 6: The effect of distribution rate.  
false positive rate is greater than 0.2, RepSTDoc_ConvNet was superior to other models. RepSTDoc_ConvNet was found to have the best performance for classifying representative spatiotemporal documents.
# GraphEPN: A Deep Learning Framework for B-Cell Epitope Prediction Leveraging Graph Neural Networks
## 3 Results
### Case Study  
To demonstrate the effectiveness of GraphEPN, we analyzed a representative protein structure, chain A of PDB ID: 6 ad8_A, from the independent test set. The performance of GraphEPN was compared against SEMA 2.0, BepiPred 3.0, ElliPro, and SEPPA 3.0.  
As shown in Figure 3, GraphEPN accurately predicted most epitope residues and minimized false positives in non-epitope regions. Its predictions closely matched the annotated ground truth, particularly in regions with complex spatial configurations. In comparison, methods like ElliPro, SEPPA 3.0, and BepiPred 3.0 struggled to achieve the same level of precision, often missing residues in structurally intricate areas or producing higher false positive rates.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Feature** & **AUC** & **AUPRC** & **BACC** & **F1** & **MCC** \\ \hline Seppa3 & 0.666 & 0.120 & 0.588 & 0.193 & 0.1181 \\ Bepipred3 & 0.711 & 0.147 & 0.622 & 0.220 & 0.1533 \\ Ellipro & 0.580 & 0.090 & 0.565 & 0.156 & 0.067 \\ Sema2 & 0.702 & 0.144 & 0.627 & 0.223 & 0.158 \\ GraphEPN & 0.765 & 0.196 & 0.657 & 0.271 & 0.214 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance of GraphEPN compared with that of available peers.  
Figure 3: Visualization of epitope predictions for a test case (PDB ID: 6 ad8_A, chain A) across multiple methods. (**a**) Reference epitopes. (**b–f**) Predictions by GraphEPN, BepiPred 3.0, SEPPA 3.0, SEMA 2.0, and ElliPro, respectively. In each model, correctly predicted epitope residues (true positives) are shown in green, residues incorrectly predicted as epitopes (false positives) are shown in red, and residues that should have been predicted as epitopes but were missed (false negatives) are highlighted in yellow. Silver represents non-epitope residues.  
To further compare the predictions across different methods at the sequence level, we provide a sequence-based visualization in Figure S4. This additional analysis offers a complementary perspective to Figure 3 by allowing for a direct comparison of predicted epitope positions along the sequence. The integration of sequence and structure-based visualizations helps to better illustrate the strengths and limitations of each method.  
GraphEPN's superior performance stems from its ability to combine discrete and continuous feature representations with structural information, enabling it to capture long-range dependencies and local interactions effectively. This advantage is evident in its ability to provide more precise and reliable predictions, even for challenging protein structures.  
This case highlights GraphEPN's robustness and accuracy, showcasing its potential as a valuable tool for epitope prediction, with practical applications in vaccine design and therapeutic antibody development.