esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Evaluation
### Algorithm Performance Results
#### 5.2.2 Scenario-Specific Performance Analysis  
Algorithm performance analysis across critical operational scenarios reveals complementary strengths that justify our multi-algorithm framework. For Gaussian noise conditions, KFTC performs well at 28.5% (Equation (1)), but HATC improves to 31.7% through intelligent selection based on Equation (4). Under linear drift scenarios, RLSTC excels at 32.8% in its optimal domain, while FLTC demonstrates superiority at 35.2% for burst error conditions involving non-linear phenomena. Most importantly, in attack scenarios, HATC achieves the highest detection rate of 35.6% through meta-learning capabilities that adapt to evolving threat patterns, as clearly illustrated in Figure 7, which shows the comprehensive performance comparison across six critical scenarios, demonstrating HATC's consistent superiority through adaptive algorithm selection.  
For visualization clarity, the 16 individual scenarios were aggregated into six representative categories:  
* Gaussian Noise: Direct mapping from Gaussian Noise scenario
* Systematic Drift: Direct mapping from Drift scenario
* Burst Attack: Direct mapping from Burst Attack scenario
* Multi-Source Noise: Direct mapping from Multi Noise scenario
* Environmental Stress: Average of six environmental scenarios  
* Temperature variations: Temp. Low (\(-40\)\({}^{\circ}\)C), Temp. High (\(+85\)\({}^{\circ}\)C)  
* Voltage variations: Volt. Low (2.8 V), Volt. High (3.8 V)  
* EMI interference and Combined environmental stress
* Attack Average: Average of six attack scenarios  
* Stealth. Attack, Desync. Attack, Burst. Attack
* Replay. Attack, Coord. Attack, Firmware. Attack  
Figure 7: Algorithm performance comparison across representative scenarios. Environmental Stress shows the average of six environmental scenarios (temperature, voltage, EMI variations). Attack Scenarios shows the average of six attack scenarios. Individual scenario data from 500 iterations per scenario.
## 4 Doppler Modeling of the Wind Component
In both cases, the measurements shows that a reduction of the wind leads to decrease of the Doppler frequency; however, the DCA does not fall down beyond 8 Hz, corresponding to about 30 cm/s surface radial velocity. Notice that this figure is comparable to Figure 1b reported in [28]. In Figure 3B it is shown the DCA, as a function of wind direction (\(\phi\)), as defined above, for three different values of wind speed: 7 m/s blue line, 5 m/s green line, 2 m/s red line. In this case, the DCA and the surface current both approach zero in the case of orthogonality between the wind direction and the radar LOS. As our focus was on the analysis of the estimated sea surface currents, we exploited the D-GMF in a direct form, which is the opposite of the method used in [28]. More specifically, given the radar geometry and the wind speed and direction provided estimated by the Bayesian retrieval involving the hindcast of the meteorological ocean model and the measured NRCS a Doppler Centroid shift \(f_{W\_DCA}\) were evaluated through the D-GMF in Equations (18)-(22) and converted to surface velocity through Equation (2).
# Estimating the Human Error Probability during Lifeboat Drills
## 5 Conclusions  
Shipboard lifeboat drills aim to improve safety, enable the timely and effective abandoning of a ship, and improve crew members' learning experience. However, instead of improved safety, numerous accidents occur during lifeboat drill performance. Besides design faults and equipment failure, human error is the most common cause of lifeboat accidents. This study used the SLIM and BN-SLIM to estimate human error probabilities while performing lifeboat drill tasks. Experts identified the related tasks and performance-shaping factors, and based on their expertise, HEP values were obtained. The most hazardous part of the lifeboat drill using SLIM, as recognised by the experts involved in this study, is lifeboat recovery, whereas the tasks confirming crew readiness for recovery and resuming recovery at the embarkation deck level, testing the manual operation of the limit switches during recovery, and disembarking the lifeboat crew were found to be the tasks with the highest probability of human error, with a probability of 4.1% each. The BN-SLIM showed that the task confirming that the release lever is properly rested and hooks locked is the task with the highest probability of human error (4.5%). Therefore, special attention should be paid during the execution of these tasks, together with improving crew members' competence and adequate equipment maintenance and inspections. The findings of this study can help ship operators and shipboard managers improve lifeboat drill safety and reduce unwanted events to a minimum.  
Conceptualisation, T.B. and N.H.; methodology, N.H.; software, T.B.; validation, T.B., N.H., M.K. and I.D.-T.; formal analysis, T.B. and N.H.; investigation, T.B., N.H., M.K. and I.D.-T.; resources, N.H.; data curation, T.B. and N.H.; writing--original draft preparation, T.B. and N.H.; writing--review and editing, T.B., N.H., M.K. and I.D.-T.; visualisation, T.B. and N.H.; supervision, T.B. and N.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
This study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of the University of Dubrovnik (Ethics approval no: 1279/24 from 24 June 2024).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The data are available from the corresponding authors on request.  
The authors would like to thank the experts involved in this study for their immense help.  
The authors declare no conflicts of interest.
Figure 10: Quantitative BN-SLIM model for task T4—recovery of the lifeboat.  
Figure 13: Sensitivity analysis for the BN-SLIM model for task T4—recovery of the lifeboat (set as the target node)—bright red colour nodes influence the target node more than pinkish-coloured nodes.
## 5 Conclusions  
This study aimed to improve the current real\(-\)time soil electrical conductivity detection device in its practical application and in view of it being highly affected by temperature, inconvenient to use, expensive, and other issues. The main contributions are as follows:  
1. The AD5941 chip was chosen to improve the four\(-\)terminal method soil electrical conductivity measurement principle, eliminating the dependence on the constant current source, optimizing the design of the soil electrical conductivity detection hardware circuit, and improving the integration degree of the prototype by using its internal DDS, ADC, and DFT modules;  
Figure 13: Comparison of prototype’s soil electrical conductivity temperature compensation effect. (**a**) The electrical conductivity of the soil sample was 805.1 \(\mu\)Scm. (**b**) The electrical conductivity of the soil sample was 3814.8 \(\mu\)S/cm.  
2. The American Society of Agricultural Engineers (ASAE) standards were followed, the conical probe structure of the four\(-\)ring metal electrode was optimized, and a new conical probe was designed. The orthogonal test was conducted to verify the reasonableness of its mechanical structure for the measurement of soil electrical conductivity, and to improve the convenience of use;
3. The detection accuracy of the prototype was improved by the temperature compensation algorithm;
4. The relevant test results showed that the relative error of measurement of the soil electrical conductivity prototype was within \(\pm\)2%; the relative standard deviation of the test data was 0.0573%, and there were no obvious differences in the data within the group; the dynamic response time was less than 50 ms; and the radial sensitivity range was 8-10 cm;
5. The results of the comparative study between the prototype and the commercialized sensor showed that the linear fit of the two measurements reaches 0.9995, with an absolute error range of \(-\)61.40 \(\upmu\)S/cm\(\sim\)23.90 \(\upmu\)S/cm, and a relative error range of \(-\)1.94%\(\sim\)1.86%. The prototype in this paper was comparable to the performance of marketed products in terms of performance indicators, and also significantly reduced the cost.  
Conceptualization, R.S.; methodology, R.S.; software, R.S.; validation, R.S.; formal analysis, R.S.; investigation, R.S.; data curation, R.S.; writing--original draft preparation, R.S.; writing--review and editing, M.Z. and R.S; supervision, M.Z. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The original contributions presented in this study are included in the article; further inquiries can be directed to the corresponding authors.  
The authors declare no conflict of interest.
# NodeVector: A Novel Network Node Vectorization with Graph Analysis and Deep Learning
## 3 Materials and Methods
### Network Analysis  
Network analysis researches the relationships and components of complex systems using nodes and edges. It examines network structure, dynamics, and properties to uncover insights and patterns [1]. Applications range from social and biological networks to transportation and computer networks [2]. By analyzing connections, interactions, and information flow, network analysis reveals system behavior, resilience, and efficiency. It identifies important nodes, communities, and pathways, aiding interventions, resource allocation, and predicting information spread [4]. This framework visualizes and interprets the interdependencies and emergent properties within networks.  
Centrality measures rank the importance of nodes in a network based on different metrics. Degree centrality focuses on the number of connections, while betweenness centrality identifies nodes that act as bridges [29]. These measures help to understand network structure and identify influential nodes. Centrality measures are valuable for studying information flow, disease spread, and the diffusion processes in complex networks [3].
## 3 Results
### Optimal Operating Point OCT Dosimetry Algorithm  
Fringe washouts in SD-OCT M-scans were evaluated with the automated dosimetry algorithm for different \(\kappa\)-values ranging from 6 to 40 and compared to levels of RPE cell damage. Data comparison took place by using the confusion matrix presented in Figure 4. The resulting statistical measures (sensitivity and specificity) are described by the ROC curve in Figure 7. The higher the AUC, the better the algorithm predicts RPE damage based on fringe washouts. The best performance was obtained for a pulse duration of 6 \(\upmu\)s (AUC = 0.97) and 14 \(\upmu\)s (AUC = 0.97). Very similar results were obtained for pulse durations of 20 \(\upmu\)s (AUC = 0.96) and 10 \(\upmu\)s (AUC = 0.93). Notably, for a pulse duration of 2 \(\upmu\)s, the performance was lower (AUC = 0.85).  
Figure 6: ED\({}_{50}\) threshold exposures (mJ/cm\({}^{2}\)) for RPE cell damage (\(ED_{50}^{RPE}\)) and fringe washouts in OCT M-scans (\(ED_{50}^{OCT}\)) for single-pulse laser irradiation (2 \(\upmu\)s, 6 \(\upmu\)s, 10 \(\upmu\)s, 14 \(\upmu\)s and 20 \(\upmu\)s). The error bars indicate the width of the normal distribution (ED\({}_{15}\) and ED\({}_{85}\)). The chart is supplemented with values from [PERSON] et al. [38] and [PERSON] et al. [39].  
The OOP of the dosimetry algorithm was interpreted by analyzing the upper left section of the ROC curve (Figure 7b) and by fitting the data points using OriginLab's Logistic function (Logistic-FitFunc). The data point that was closest to the 45\({}^{\circ}\) OOP reference line was determined as \(\kappa\)-value = 13 at a laser pulse duration of 6 us (sensitivity: 0.89, specificity: 0.94) according to the assumption that specificity and sensitivity are weighted equally. Highly similar results were obtained for \(\kappa\)-values of 14 (sensitivity: 0.87, specificity: 0.96) and 12 (sensitivity: 0.89, specificity: 0.93).
# Computational-Intelligence-Based Spectrum-Sharing Scheme for NOMA-Based Cognitive Radio Networks
## 2 System Model and Problem Formulation
### NOMA-Assisted Power Allocation Scheme  
In the proposed NOMA-assisted transmit power allocation scheme, the whole communication process is divided into two time slots. First, information for the PBS and all SUs is transmitted via the NOMA relay SR to the destinations. Then, one best SU retransmits the PBS's signal while the SR simultaneously receives the information for the PBS for the next time slot. In the proposed scheme, MRC is employed to guarantee reliable transmission of the primary signal, which improves the QoS of the system. The above communication process is illustrated in Figure 2 and explained in further detail below.
# An Automatic ECG Signal Quality Assessment Method Based on Resnet and Self-Attention
## 1 Introduction
Attention mechanisms have attracted a lot of interest recently in fields such as image processing and natural language processing, including tasks such as text translation and image classification [27; 28]. However, it is seldom applied in the area of one-dimensional signal processing. In this work, we develop a self-attention module for processing one-dimensional ECG signal data based on QRS waveform similarity. The module creates an attention map by computing pairwise relationships between the channels, then weighting all the features to aggregate information about the distant dependencies between channels. The module employs a 1x1 convolution operation at the input side, which can flexibly reduce the number of channels, thus reducing the calculation workload of the attention module. To avoid subjective judgments and redundant information that may be introduced by manual computation of features, as well as the problem of network performance degradation, this paper adopts the idea of a residual network to build a network model. The network incorporates the proposed self-attention module and improves the structure and parameters of the residual model to adapt to the processing of one-dimensional signals. Our test results on the PhysioNet/Computing in Cardiology Challenge 2011 (Cinc2011) show that the model can achieve efficient and high-quality ECG signal evaluation with low resource consumption. It reduces the workload of the ECG medical experts and improves the efficiency and accuracy of cardiovascular disease diagnosis.
# State-of-the-Art Mobile Radiation Detection Systems for Different Scenarios
## 3 Mobile Platforms  
When choosing a mobile platform to carry a radiation detection system for a specific task or scenario, it is important to know what are the requirements that must be taken into account. Requirements, such as the weather sensitivity of the vehicle, payload capacity, and how that affects its performance (e.g., range, operational time), cost (initial investment and operational cost), ease of operation, ease of decontamination, and spatial resolution attainable (e.g., for mapping purposes) [18], can impact on the quality and effectiveness of the radiation measurements. Therefore, to choose the right mobile platform, it is necessary to know its advantages and limitations.  
Mobile platforms can be divided into ground-based and air-based. Each platform may be either manned or unmanned.  
Compared to manned ground vehicles or aircrafts, unmanned systems have several advantages, such as performing high-risk tasks (e.g., high radiation, contaminated areas or danger of explosion), more cost effective, and long-time survey and monitoring capability. Depending on the degree of human intervention on the robot's decision (robot's autonomy), they can be fully teleoperated (remote operated vehicle), semi-autonomous (aka supervisory control), or autonomous. Search and rescue robots, the most similar domain applicable to the radiation measurement scenarios, are normally either teleoperated or semi-autonomous [18].
###### Abstract  
Models of particle density and of organic carbon and chlorophyll-a intraparticle concentration were applied to particle size distributions and particle real refractive index distributions determined from flow cytometry measurements of natural seawater samples from a range of UK coastal waters. The models allowed for the estimation of suspended particulate matter, organic suspended matter, inorganic suspended matter, particulate organic carbon, and chlorophyll-a concentrations. These were then compared with independent measurements of each of these parameters. Particle density models were initially applied to a simple spherical model of particle volume, but generally overestimated independently measured values, sometimes by over two orders of magnitude. However, when the same density models were applied to a fractal model of particle volume, successful agreement was reached for suspended particulate matter and both inorganic and organic suspended matter values (RMS%E: 57.4%, 148.5%, and 83.1% respectively). Non-linear organic carbon and chlorophyll-a volume scaling models were also applied to a spherical model of particle volume, and after an optimization procedure achieved successful agreement with independent measurements of particulate organic carbon and chlorophyll-a concentrations (RMS%E: 45.6% and 51.8% respectively). Refractive index-based models of carbon and chlorophyll-a intraparticle concentration were similarly tested, and were also found to require a fractal model of particle volume to achieve successful agreement with independent measurements, producing RMS%E values of 50.2% and 45.2% respectively after an optimization procedure. It is further shown that the non-linear exponents of the volume scaling models are mathematically equivalent to the fractal dimensionality coefficients that link cell volume to mass concentration, reflecting the impact of non-uniform distribution of intracellular carbon within cells. Fractal models of particle volume are thus found to be essential to successful closure between results provided by models of particle mass, intraparticle carbon and chlorophyll content, and bulk measurements of suspended mass and total particulate carbon and chlorophyll when natural mixed particle populations are concerned. The results also further confirm the value of determining both size and refractive index distributions of natural particle populations using flow cytometry.  
Keywords:forward modeling; suspended matter; marine particles; fractal structure; organic carbon; chlorophyll-a +
Footnote †: journal: Applied Physics Letters
## 5 Evaluation
### Experimental Results
#### 5.1.1 Energy Consumption in Ideal Environment  
The first evaluation compares the total energy consumption when applying our systems in ideal manufacturing plants of different sizes (small, medium, and large). For small plants, we consider 400 k machine messages each day, in medium plants about 1.5 Mio, and in large plants around 2.5 Mio machine messages per day (cf. [6]). In the evaluation, we compared the modules of our GreenCC (LightCC and FullCC) with Apache Flink ([[https://flink.apache.org/](https://flink.apache.org/)]([https://flink.apache.org/](https://flink.apache.org/)), accessed on 14 September 2023) and our newly developed automaton. For this we kept the concepts of GreenCC and used the Flink framework for stream handling. Table 1 presents consumption in kilowatt-hours (kWh) and costs in cent. Table 2 shows our evaluation results of the climate footprint in grams of carbon-dioxide equivalents (gCO\({}_{2}\)_e_). We have already presented part of the evaluation in [6], therefore, we will not go into detail on each point. The main results from [6] are that energy and costs can be saved by using more efficient processes. In a medium-sized plant, energy consumption can be reduced by a factor of more than 0.3 in this way. (cf. Flink (all) vs. FullCC (all)). This reduction does not affect the accuracy of the detection. The reduced consumption also lowers operating costs and the climate footprint. (medium-size plant in EU about 676 gCO\({}_{2}\)_e_ less). In the past evaluation, we also observed that the deciding factor between the approaches is not due to the semantic transformation and validation, but to the overhead that a framework like Apache Flink brings with it. The consumption values of our automatons are highlighted in blue. The results show that the automaton even outperforms the FullCC with a larger range of functions. The difference in a medium-sized plant is 1.116 kWh, which corresponds to a factor of 0.24. For the climate footprint, this difference results in a reduction of 293 gCO\({}_{2}\)_e_ (assuming an EU average of 262 gCO\({}_{2}\)_e_/kWh). Overall, the first evaluation shows that the automaton structure has a positive influence on the computing resources required. One main reason for this is the adapted internal data management through which data are validated as quickly as possible in order to forget parts that are no longer needed. In our next analysis, we will look at the memory consumption during the analysis of a data set. \begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Approach**} & **Small Plant per Day** & **Medium Plant per Day** & **Large Plant per Day** \\  & **(Costs\(\backslash\)Day)** & **(Costs\(\backslash\)Day)** & **(Costs\(\backslash\)Day)** \\ \hline Flink & 1.898 kWh & 7.116 kWh & 11.860 kWh \\ (1\&2) & (24.04 Cent) & (90.16 Cent) & (150.27 Cent) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Energy consumption in kilowatt-hours (kWh) and operating costs for small, medium, and large manufacturing plant on a daily basis.
## 2 Materials and Methods
### Ground Measurements
\begin{table}
\begin{tabular}{c c c} \hline
**Category** & **Parameter** & **Method or Device** & **Interval** \\ \hline \multirow{3}{*}{Spectrum} & Irradiance (mW/m\({}^{2}\)/nm) & \multirow{3}{*}{HR2000+} & \multirow{3}{*}{\(\sim\)6 min} \\  & Radiance (mW/m\({}^{2}\)/nm/sr) & & \\ \hline \multirow{6}{*}{Meteorology} & Precipitation (mm) & \multirow{3}{*}{HOBO U30 USB Weather} & \multirow{3}{*}{5 min} \\  & Pressure (kPa) & & \\ \cline{1-1} \cline{3-3}  & Solar radiation (SR, 300–1100, W/m\({}^{2}\)) & & \\ \cline{1-1} \cline{3-3}  & Air temperature (Ta, \({}^{\circ}\)C) & & \\ \cline{1-1} \cline{3-3}  & Relative humidity (RH, \%) & & \\ \hline Soil & Moisture (20 cm, m\({}^{3}\)/m\({}^{3}\)) & & \\ \hline Radiation & PAR (400–700 nm, umol/m\({}^{2}\)/s) & & \\ \hline \multirow{6}{*}{Vegetation} & Leaf area index (LAI, m\({}^{2}\)/m\({}^{2}\)) & \multirow{6}{*}{LAI-2200C} & \multirow{6}{*}{\(\sim\)1 week} \\  & Mean tilt angle (MTA, \({}^{\circ}\)) & & \\ \cline{1-1} \cline{3-3}  & Chlorophyll content (Chl, \%) & & \\ \cline{1-1} \cline{3-3}  & Relative water content of leaf (RWC, \%) & & \\ \hline \end{tabular}
\end{table} 
Table 2: Parameters of winter wheat measurements at Fangshan Comprehensive Experimental Station in 2017 in Beijing, China.