esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Mechanism of Antifungal Action of LIN  
The mode of action of LIN was investigated to assess whether its antifungal effect is due to interaction with the cell wall structure of _Candida_ spp. (via the sorbitol test) and/or with the ion permeability of the organism's membrane (via the ergosterol test).  
In the sorbitol test, MIC determinations were carried out with and without 0.8 M sorbitol, a known osmoprotectant. The MIC of a cell wall-damaging compound is expected to increase in the presence of this compound. Our results, shown in Figure 2, indicated that the MIC value of LIN did not change in the sorbitol medium after the 7-day incubation. This suggests that LIN most likely does not exert its antifungal effect at the level of cell wall. The MIC value of nystain (negative control), which acts on the fungal cell membrane, remained the same in the presence of sorbitol.  
Figure 2: The increase in the MIC values (x MIC) of LIN and NYS in the presence of ergosterol and sorbitol toward selected reference _Candida_ spp. strains.  
The ergosterol assay is based on the binding of exogenous ergosterol to the tested compound, thus preventing its complexation with ergosterol within the cell membrane. The result is an increase in MIC value. Our data, presented in Figure 2, showed 2-4-fold increased MIC values of LIN in the medium with ergosterol compared to those without ergosterol. Similarly, MIC values of nystatin, a known antifungal agent acting via membrane ergosterol binding (positive control), exhibited an 8-16-fold increase. The obtained results suggest that the mode of LIN action may be related with binding to ergosterol in the membrane, leading to an increase in permeability and cell death.
# Risk-Quantification Method for Car-Following Behavior Considering Driving-Style Propensity
## 1 Introduction
Based on the field strength theory, [PERSON] [14] used the game theory combination assignment method to determine the impact of human-vehicle-road-environment interaction on risk, expressed it as a field strength value, and calculated the field strength value of the surrounding vehicles on the self-driving vehicle to arrive at a risk assessment. In summary, most current car-following risk quantification and early warning strategy development only considers the theoretical conflict risk based on theoretical conditions such as vehicle dynamics and does not adequately consider the uncertainty of the car-following behaviors of surrounding drivers with different driving styles, nor that aggressive drivers will increase the car-following risk. To effectively quantify the risk of microscopic car-following behavior in traffic flow, this study first establishes a car-following model based on the safety potential field theory, which systematically portrays the changing trend of the safety potential field of the vehicle under different speed and acceleration values. Simultaneously, it combines driving style and car-following safety, accurately characterizes short-term driving style based on time-series data, constructs different characteristic index groups according to different driving roles, and integrates them into quantitative indicators of car-following risk. Finally, a quantitative model of the integrated car-following risk model, under the safety potential field, is established by considering the real-time risk exposure level and real-time risk severity, and the risk level is categorized. The LightGBM algorithm predicts the risk level in real time under car-following conditions, warns the driver according to the risk level, provides warning information containing qualitative style descriptions, and assists the driver in avoiding risk.
# Residual Vibration Reduction in Flexible Systems Based on Trapezoidal Velocity Profiles
## 5 Experimental Validation  
### Overview of Experimental Design  
The experimental validation was conducted using two different industrial robots, UR5e and AUBO-i5, to demonstrate the broad applicability of the proposed method. As previously mentioned, the method acts on the motion planning level and is built upon the trapezoidal trajectory inherently integrated within commercial robotic control systems. Therefore, it can be readily implemented on general industrial robots. The experiments were designed to evaluate the effectiveness under varying payload conditions and provide performance comparisons across different motion profiles. In the first experiment, a relatively light payload was used, and the optimized trajectories with different vibration suppression conditions were compared to the unoptimized trapezoidal trajectory provided by the robot. In the second experiment, a more complex and heavier payload was used, and the optimized trajectories were further compared with benchmark methods. In this study, since the robots feature rigid links and possess payload capacities significantly lower than their own weight, changes in payload have negligible effects on the robots' dynamic performance. The vibratory response is predominantly determined by the fundamental flexible mode of the payload. The proposed method optimizes trajectories based on the measured modal frequency to eliminate harmonic components near the resonant mode.
# Uplink Transmit Power Control for Single-Carrier Grouped FDMA with Iterative Multiuser Detection
###### Abstract  
We consider an uplink power allocation scheme for single-carrier frequency-division multiple access (FDMA) with iterative multiuser detection, called single-carrier grouped FDMA (SC-GFDMA). SC-GFDMA is a non-orthogonal scheme in which several users share a single time-frequency resource. Hence, the uplink signal of a user can be regarded as both a signal and a source of interference. The signal power of each user should be sufficiently high to ensure reliable signal detection and sufficiently low to suppress inter-user interference. That is, the transmit power of each user should be adjusted appropriately to achieve high spectral efficiency. In this context, a power control method for an uplink SC-GFDMA system is proposed by analyzing the signal-to-interference-plus-noise ratios of users sharing each time-frequency resource. In particular, the uplink spectral efficiency is improved by limiting the transmit power of each user according to a criterion derived using a semi-analytic method called signal-to-noise ratio-variance density evolution. Simulation results demonstrate that the proposed method can significantly increase the spectral efficiency of the system, even with a considerably reduced total transmit power.  
SC-FDMA; iterative multiuser detection; grouped FDMA; power control; spectral efficiency +
Footnote †: journal: Applied Sciences  
_Article_
## 5 Case Studies
### Results and Discussion
#### 5.2.4 Results for the Fourth Case Study and Comparison  
To illustrate the performance of IFA, the comparison of IFA with FA, GA and PSO was performed. Here, the training sets and testing sets used in the second case study were applied again. The difference is that only the method based on RCMIs was implemented. Results obtained by IFA and other methods in the fourth case study are presented in Figure 8.  
Figure 7: Effect of changing in size of training set in the third case study.  
Compared with other models, it can be obviously observed that the GA-WSVM model has the worst performance. The curve of GA-WSVM model is very large, ranging from 71.96% to 90.00%, and it has the lowest point. PSO-WSVM and FA-WSVM models have a good performance, but their curves are still relatively large, ranging from 74.82% to 92.73% and 80.36% to 95.23% respectively. Whereas the IFA-WSVM model has a better performance and outperforms other models, whose curve tends to more gentle and has the highest recognition rate throughout the different experiments. In conclusion, the results indicate that the proposed IFA-WSVM model presents better performance in state recognition than the other three methods, even when the size of the training set is small.
## 3 Results
### EP Studies (EAM Mapping and Arrhythmia Inducibility Tests)
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Animal ID** & **Intracardiac EGM Waves** & **Bipolar Voltage (mV)** & **QRS Interval (ms)** & **Q-T Interval (ms)** \\ \hline Healthy Ref. & & 3.00 & 56 & 290 \\ \hline Pig 4 & & 0.29 & 65 & 338 \\ \hline Pig 4 & & 0.48 & 59 & 338 \\ \hline Pig 4 & & 0.33 & 63 & 351 \\ \hline Pig 4 & & 0.5 & 47 & 293 \\ \hline Pig 3 & & 0.48 & 56 & 307 \\ \hline \hline Pig 4 & & 0.48 & 56 & 307 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: QRS and QRS-T intervals and exemplary iEGM waves for points of low bipolar voltage (<0.5 mV) selected from pig #3 and pig #4, along with reference intervals from healthy pigs. appearance of low voltage intracardiac recordings at the fibrotic regions within 3 QRS beats of the projected bipolar voltage signal, suggesting acceptable reproducibility. Figure 9 shows examples of recorded iEGMs and reference 12-lead ECGs in pig #3, during: sinus rhythm (650 ms R-R interval), pacing at 500 ms and induced ventricular fibrillation, VF. In both animals in Group 2 (i.e., pig #3 and pig #4) VF was induced via rapid pacing (<300 ms); however, it could not be induced in the healthy control animal, regardless of the pacing rate. These observations, corroborated by the recorded bipolar voltage maps and the MRI-derived fibrosis maps suggested that the small patches of diffuse fibrosis identified in histopathology images acted as small anatomical obstacles and perturbed the normal propagation of electrical waves through the heart. These scattered patches likely broke the propagating waves into smaller spiraling waves, and in conjunction with their corresponding prolonged QRS-T intervals, favored a chaotic electrical propagation specific to fast and lethal VF. Note that these two pigs were not defibrillated, as such a maneuver was out of the scope of this study.
# Deep Learning for Visual Leading of Ships: AI for Human Factor Accident Prevention
## 2 Materials and Methods  
### Transas 5000 Simulator of Navigation  
The Higher Technical School of Nautical and Marine Engineering at the University of A Coruna (UDC) is equipped with the Navi-Trainer Professional 5000 [10, 11], a state-of-the-art simulator that replicates a full navigation bridge. Developed by the Norwegian company Transas, this system is primarily used to train deck students preparing to become future officers and captains.  
Thanks to its advanced capabilities, it supports the training and certification of watch officers, chief mates, masters, and pilots operating merchant and fishing vessels over 500 gross tons, in accordance with the IMO STCW 78/95 Convention (International Maritime Organization). It is particularly effective for specialized training courses and is also a valuable tool for reconstructing and analyzing complex navigational scenarios, including real-life maritime emergencies.  
As shown in Figure 1, the NTPRO 5000 combines hardware and software to create highly realistic navigation bridge environments. The system is managed from an instructor's station and operates over a local network of interconnected standard personal computers.  
The simulator software includes multiple modules: a network operation manager; systems for computing mathematical models of own ships, target vessels, drifting objects, tugboats, 3D wind-generated sea states, mooring lines, and fenders; the main instructor interface; coming displays; visual channels; and interfaces with actual ship equipment.  
It also features preloaded and regularly updated databases, including a 3D visual scene library for various maritime regions, radar scene libraries, and a broad range of vessel-specific mathematical models.  
This simulator holds a Statement of Compliance with the Class A Standard for the Certification of Maritime Simulators [12] Standard No. 2.14, issued by DNV (Det Norske Veritas) in accordance with STCW Convention Regulation I/12.  
One of its key advantages is the instructor's ability to fully customize training scenarios. They can configure different ship types, navigation zones, weather and sea conditions, and time of day, enabling a highly realistic visual and acoustic simulation. Properly calibratedship models ensure that vessel behavior during navigation, berthing, towing, and other port operations reflects real-world dynamics, even under adverse conditions.
## 3 System Model  
We start this research from the simplest case, i.e., baseband communications, to avoid missing all aspects of this research field. We consider a baseband communication system in which symbol duration is much smaller than the coherent time of the channel, and the Doppler effect is negligible. This assumption is unavoidable unless otherwise the baseband pulses will be broadened at the receiver due to the Doppler effect, and a high amount of interference corrupts the communication.  
Consider a modern baseband communication system. The transmitter encodes data bits using a low-density parity-check (LDPC) encoder. Pilot symbols are inserted to enhance the performance of the receiver. This is followed by a binary pulse amplitude (B-PAM) modulator. The coded modulation symbols \(\mathbf{c}=(\mathbf{c_{0}},\mathbf{c_{1}},...,\mathbf{c_{K-1}})\) are pulsed-shaped using the root-raised cosine filter and then transmitted over an additive white Gaussian noise (AWGN) channel impaired by path overlapping.
# Machine-Learning-Based Darknet Traffic Detection System for IoT Applications
## 2 Related Work
### Use of Neural Networks in Recent Works  
Advances in artificial intelligence have enabled digital systems to detect and identify Darknet activity on their own. A generalized strategy for detecting and categorizing Darknet traffic using Deep Learning was proposed in [46]. To recognize network traffic more correctly, the researchers used adapted convolution long short-term memory and extreme gradient boosting as feature selection techniques. The results show that the suggested approaches detect and categorize Darknet traffic with an accuracy of 89% for categorization and 96% for detection. To identify Darknet traffic, ref. [47] used popular machine-learning classification techniques. A receiver operating characteristics (ROC) analysis was combined with a feature significance analysis for the best classifier. The studies used the new dataset CICDarknet2020, and the classifiers were trained to classify binary and multi-class data. The random forest method was used to produce an average prediction accuracy of over 98%. Using ensemble machine-learning algorithms on the CIC-Darknet2020 dataset, ref. [48] was able to differentiate Darknet traffic from benign traffic with 98% accuracy. Furthermore, with 97% accuracy, the researchers recognized the sort of program running beneath the Darknet traffic. They also used a game-theoretic method to demonstrate the impact of selected features and interpret the output of machine-learning models in order to better understand Darknet traffic behavior. The study presented in [49] proposes a weight-agnostic neural network framework for Darknet traffic and network management, with the goal of automating the suspicious intent recognition process in real time. According to the authors, the presented approach allows for a more customized pattern recognition system to cope with changing situations without previous training. The study proposed in [50] presents a CNN-based classification system that can recognize both protocols and applications. The proposed method employs a two-stage, two-label classification system. The protocol used for encrypted traffic is classified in the first step. The second stage employs the corresponding classifier to categorize applications based on the traffic protocol. On the CICDarknet2020 dataset, experimental findings reveal that the suggested approach yields an accuracy of about 97.6%.
## 4 Discussion
### Relationship with the State-of-the-Art on Specialized Fields  
Bibliometric research on factors influencing research productivity in cybersecurity, blockchain, and privacy has traditionally focused on publication counts, top contributors, and collaboration patterns. Previous studies have identified the United States and China as leading producers across these fields, noting a significant concentration of output among a few countries. Various factors influencing productivity have been suggested, including economic conditions, national priorities, technological infrastructure, government strategies, and research funding.  
Our study presents several original contributions to this field of research. By employing regression models to analyze the factors influencing research productivity, it provides quantitative measures of their influence, allowing for more precise comparisons. This approach enables a direct comparison of how different factors affect productivity across blockchain, privacy, and cybersecurity, revealing field-specific patterns.  
The study's distinction between the impact of general research spending and other economic factors offers more detailed insights into the role of dedicated research funding. This specificity allows for a clearer understanding of how different types of investment affect research output.  
Our study also sheds new light on the influence of political systems on research output. The varying impact of the liberal democracy index across the fields, particularly its strong influence on privacy research, provides novel insights into the relationship between political structures and research focus.  
One of the most striking findings is the unexpected negative relationship between average years of schooling and research output in blockchain and privacy. This challenges assumptions about the role of general education in these fields and suggests that specialized knowledge or entrepreneurial activity may be more crucial drivers of productivity.  
By including multiple factors in a single regression model, the research offers a more comprehensive view of how various societal elements interact to influence research productivity. Unlike many previous studies that examined absolute numbers, this analysis focuses on per capita output, providing a deeper understanding of country-level productivity.
# Ship Route Oil Spill Modeling: A Case Study of the Northeast Brazil Event, 2019
## 3 Results
Importantly, the estimated biodegradation process in the model showed growth of up to approximately 60%, and evaporation did not exceed 20% of the total spilled volume. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Experiment**} & **Start Date of** & **Minimum** & **Arrival Date on** & **Maximum Oil** \\  & **Experiments** & **Arrival Time** & **the Coast** & **on the Coast** \\  & **(day/month)** & **(days)** & **(day/month)** & **(tons)** \\ \hline
1 & 09/07 & 54 & 01/09 & 34.2 \\
**2** & **12**/07 & **18** & **30**/07 & **593.8** \\
3 & 12/07 & 76 & 26/09 & 4. The probabilistic analysis of surface oil occurrence conducted based on experiment 22 is shown in Figure 6. The results revealed a low probability (between 5 and 10%) of oil reaching the northern coast of the State of Rio de Janeiro (RJ), which is the southernmost point of oil observation along the coast and was not found in the deterministic experiment. Nevertheless, the model was able to demonstrate that, albeit low, the probability of oil arrival on the coast in this region was possible, as the observation revealed. The study indicated that the coastline between the states of Ceara (CE) and the northern coast of Bahia (BA) exhibited high susceptibility to oil presence (>50%) in this scenario, and that the possibility of oil arrival extended along the entire coastline, where stains and pellets were identified. Figure 4: Total oil concentration in the water column (<100 m) on 29 August 2019, with emphasis on the arrival on the coast (dots in cyan) and the north–south directions of oil dispersion black arrows) for experiment 22. The black dots show the locations on the coast where the oil arrived at the end of the simulation on 2 December 2019. Figure 5: Temporal series of the oil mass balance for experiment 22 considering the volume of 3 internal tanks (42,753 m\({}^{3}\)).
## 2 Methods and Tools
### Comparative Analysis of Geohazard Monitoring Methods  
Table 2 provides a comprehensive overview to streamline the discussion strategy for each geohazard monitoring method. It systematically evaluates satellite, ground-based, and integrated techniques across five key parameters: (1) Introduction to the Method, (2) Spatial and Temporal Resolution, (3) Data Acquisition, (4) Processing Framework, and (5) Integration Opportunities. The table enhances clarity and consistency by presenting detailed quantitative insights into the strengths, limitations, and synergies of each approach.  
By highlighting how these methods complement one another, Table 2 serves as a concise and informative guide for understanding their collective potential in advancing geohazard monitoring and analysis.
## 3 Results
### Pockmark Field
#### 3.1.3 Gas in Marine Sediments  
The study of the seismic profiles showed the existence in the area of various 'abnormal' acoustic characters, which are direct and indirect evidence for the presence of fluids in the sediments. The most frequently occurring acoustic characters are the Acoustic Turbid Zone (ATZ) (Figure 10a,b) [16; 30; 62; 63; 64; 65] and Enhanced Reflectors (ER) (Figure 10a,b) [64; 65; 66; 30]. Rarely, Intrasedimentary Gas Plumes (IGP) (Figure 10b) [62; 63] and Seabed Doming (D) (Figure 10b) [64; 67] were observed.  
Figure 9: **(a)** Map of PGPF showing the spatial distribution of the acoustic types. **(b)** Representative seismic profiles of each Acoustic Type (AT) recognised by a high-resolution sub-bottom profiler (TWTT two-way travel time).  
The most extended gas-related acoustic character observed in the seismic profiles is that of ATZ. These zones result from the attenuation of acoustic energy by the presence of gas bubbles within the interstices of the sediments. The gas front, which is the top of ATZ, shows up as a single strong reflection or as a combination of Enhanced Reflectors, indicating the upward migration of the gas (Figure 10).  
Enhanced reflectors (ER) appear as coherent high-amplitude reflections with abrupt terminations. They have been observed in various stratigraphic levels in the SS I, suggesting the upward migration of gas (Figure 10). In many cases, Enhanced Reflectors are above the top of the ATZ or inside the ATZ (Figure 10).  
ATZs are a widely spread acoustic character in the pockmark field and are mostly found below pockmarks in both sectors of the field (Figure 11a). Exceptions are the two limited areas (A and B) with a total lack of ATZ and ER; area A is located at the southern end of the northern sector, and area B is at the central part of the southern sector (Figure 11b). The Pleistocene/Holocene boundary has been clearly identified in both areas.  
Figure 11: (**a**) Map showing the spatial distribution of Acoustic Turbid Zones (ATZ) and Enhanced Reflectors (ER) and their relationship with the presence of pockmarks. (**b**) Map showing the depth below the seabed where the top of ATZ and ER has been recorded. A and B delimitate areas without ATZ and ER.  
Figure 10: Representative high-resolution seismic profiles (**a,b**) showing the presence of Enhanced Reflectors, Acoustic Turbid zones, Intrasedimentary Plumes and Seabed Doming due to gas migration to the upper seismic sequence (I). (ER: Enhanced Reflector; ATZ: Acoustic Turbid Zone; IGP: Intrasedimentary Gas Plumes; D: Seabed Doming; F: Fault; HOL/PL: Holocene Pleistocene boundary; TWTT two-way travel time; SSI: upper seismic sequence; SSII: lower seismic sequence).  
In general, it seems that the pockmarks are formed in the shallowest parts of the area where ATZ and ER have both been observed (Figure 11a). The pockmarks at the northern sector, at the proximity of AT.F, are solely linked with ATZ since a lack of ER is observed (Figure 11a).