esa-sceva/satcom-chunk-collection huggingface dataset:
# A Graph-Cut-Based Approach to Community Detection in Networks
## 4 Results
### Example 3: The Social Network of Bottlenose Dolphins  
The third example network is the social network of bottlenose dolphins that was studied by [PERSON] [73]. [PERSON] conducted a study about a social network of bottlenose dolphins residing in the Doubtful Sound, Fiordland, New Zealand, from November 1994 to November 2001. Based on the observations and study, 62 dolphins were formed into a social network, and it is shown in Figure 4a [74].  
As can be seen from Figure 4a, the community structure of the dolphin network is not random, but it is not obvious, either. In other words, it seems that there is a weak community structure in the network, and different people may have different opinions on the structure as well as the number of communities. As mentioned before, it is hard to find a universally agreed answer for the community structure of a network, and this example illustrates this difficulty.  
Figure 4b shows the result of MCCD application to the network. The modularity value achieved its maximum of 0.4021 after three iterations; hence, MCCD detects four  
\begin{table}
\begin{tabular}{c c c c c c} \hline Algorithm & GN & FN & CNM & DA & MCCD \\ \hline Modularity & 0.401 & 0.381 & 0.381 & 0.419 & 0.372 \\ \hline \end{tabular}
\end{table}
Table 1: Modularity values for the karate club network of a few well-known community detection algorithms and MCCD: [PERSON] (GN) [25]; [PERSON] (FN) [27]; [PERSON], Newman, [PERSON] (CNM) [42]; [PERSON] (DA) [72]. Data from [25; 27; 46; 72].  
Figure 4: The social network of bottlenose dolphins in Doubtful Sound, New Zealand, and the application of MCCD to the network. (**a**) The social network of bottlenose dolphins; (**b**) the communities detected by MCCD; (**c**) the betweenness centrality values \(c_{B}(v)\); (**d**) the modularity values after each iteration of DIVIDE-INTO-TWO.  
communities shown in the figure. The betweenness centrality values of the vertices and the modularity values after each iteration are shown in Figure 4c,d, respectively. It turns out that the modularity values of the first few iterations are similar, and this implies that it is hard to tell which structure is best for community detection. Even though MCCD generates the four community structure, structures of slightly different numbers of communities are also plausible. [PERSON] presented a sociogram with three groups in his original paper [74]. In fact, the whole network visually seems to have two large groups, where the network is divided into two groups mostly by the gender of dolphins: males and females.
# Heuristic Routing Algorithms for Time-Sensitive Networks in Smart Factories
## 4 Improved Genetic Algorithm to Solve TT Flow Routing Problem
### Key Innovations and Contributions of GATTRP  
To the best of our knowledge, GATTRP transforms, for the first time, the TT transmission problem in a smart factory TSN into an MTSP problem for optimization, which is the key innovation of this paper in terms of the TT routing problem. In addition, in contrast to existing genetic algorithms, GATTRP adds an elite chromosome mechanism that alleviates the problem of the GA tending to fall into premature and difficult-to-search-for solutions. With this optimization, GATTRP has a higher probability of obtaining better results after the iterations.
## 2 Background  
The movement accuracy of the working system is frequently crucial to the accuracy and efficiency of technological processes. In the case of machines with boom systems and hydrostatic power systems this issue is of particular importance. Prior research and development works performed by the author demonstrated that the implementation of new monitoring or control solutions on the level of hydrostatic power system does not always guarantee the expected operating efficiency parameters [19]. Contradictory requirements defining on the one hand the process speed and on the other hand the positioning accuracy of the work system represent a significant challenge to modern hydrostatic power systems. Moreover, the introduced changes should have a positive effect on the obtained efficiencies of the system, and this requirement practically eliminates throttling control, which is commonly used in classic solutions in order to reach the required working movement accuracies. Research works carried out in this direction resulted in the development of new self-contained electro-hydraulic actuator (EHA) systems whose operating parameters, including the movement accuracy has been improved in comparison with the parameters of the classical solution [8; 16]. However, the described and tested solutions had complex hydraulic control systems and hydraulic accumulators. Therefore, further work in this area was aimed at reducing the complexity of the EHA hydraulic control system, which translated into both the reduction of cost and the limitation of space necessary for installation [18]. Minimizing the number of hydraulic control elements thus seems to be beneficial and the development of such hydraulic systems is a promising research direction.
## 5 System Model in Two-Hop Case
### Two Proposed Schemes
The coding rate is determined based on that and the estimated SNR from Figure 8:4 As the reason for using the desired PER and \(L_{info}\), it is possible to accurately obtain the desired BER for determining the coding rate from the Equation (14) since the required QoS (desired PER) is used. For example, in a case where the desired PER is \(10^{-2}\) and \(L_{info}\) is 400 bits, the desired BER is calculated as \(2.5\times 10^{-5}\). Here, if the estimated SNR is 5 dB, the coding rate is determined to be \(r_{c}=1/2\) as shown in Figure 8:5
## 3 AI Implementations in Maritime Transport Systems
### AI in Crew Resource Management
For instance, AI systems that monitor environmental conditions and vessel performance can alert the crew to dangerous situations, such as approaching storms or mechanical failures, allowing for timely preventive measures. Human error remains a significant factor in maritime accidents. AI technologies help mitigate this risk by supporting decision-making processes and automating routine tasks. For example, AI-driven navigation systems can reduce the cognitive load on the crew by handling complex calculations and providing clear recommendations, thereby minimizing the chances of errors caused by fatigue or information overload. Furthermore, AI can continuously monitor crew performance and provide reminders or warnings when deviations from standard procedures are detected. For instance, Kongsberg Maritime's K-Sim platform offers highly realistic training scenarios ranging from standard operational procedures to emergency responses. K-Sim's AI capabilities allow it to adapt to the trainee's performance, providing tailored feedback and enhancing the learning experience. Research has shown that seafarers trained with K-Sim exhibit improved operational performance and decision-making skills, contributing to safer maritime operations [17; 40; 41]. The Nautilus 3D is an AI-based simulation platform designed to enhance maritime training by providing immersive, interactive scenarios. It uses AI to create realistic training environments that adapt to the actions of the trainees. The platform has been used to train crews on various aspects of maritime operations, including navigation, emergency response, and maintenance procedures. Evaluations of the Nautilus 3D system have demonstrated its effectiveness in improving crew preparedness and reducing the incidence of errors during actual operations [38; 42; 43]. In conclusion, the application of AI in crew resource management represents a significant advancement in maritime safety. Through enhanced training simulations and real-time decision support, AI technologies equip crew members with the skills and information needed to operate safely and efficiently. The ongoing development and adoption of these technologies promise to further reduce the risk of accidents and improve the overall safety of maritime transportation.
# Security and Independence of Process Safety and Control Systems in the Petroleum Industry
## 4 Technological Trends, New ICT Systems, and Industrial Internet of Things (IIoT) Solutions
### Data Diode  
A data diode is a physical network component that, when connecting network \(A\) with network \(B\), can guarantee that data can flow from \(A\) to \(B\), but not from \(B\) to \(A\). There are a number of different ways to implement a data diode; an early suggestion from [PERSON] and [PERSON] [19] involved a trusted process that wrote to a communication buffer (i.e., a queue), and another trusted process that read from the other end of the communication buffer.  
However, many such data diodes were designed to ensure confidentiality in situations with data moving from a lower classification level (e.g., \"Confidential\") to a higher classification level (e.g., \"Secret\"). This is in line with the Bell-LaPadula model; however, in our case, the situation is the other way around: we want to communicate data from a high-integrity zone to a zone with lower integrity requirements; and we are not concerned with confidentiality but concerned that the equipment in the latter zone cannot affect equipment in the former (functional independence).  
The solution from [PERSON] and [PERSON] was quite complicated, and later solutions exemplified by [PERSON] and [PERSON] [20] were rather based on the use of an LED and a phototransistor. In principle, this is the same as taking a fibre-optic interface where you physically remove the return fibre.There are a large number of data diodes from different providers that have been evaluated according to the Common Criteria at the highest level [21].  
The data diode is an attractive solution in the way that it can physically guarantee that \(A\) is independent of \(B\) if the data diode represents the only connection between the two. In the meantime, it may present a problem if there is a need for \(A\) to be updated or reconfigured from \(B\).  
In this case, there would be a need for some way to bypass the data diode; on a device, this may be that maintenance personnel physically travel out to make the changes, but this of course appears to be very cumbersome. In practice, this means that many people who use data diodes at the same time create \"additional solutions\" that make it possible to connect to \(A\) from \(B\) (and elsewhere); this would mean that the independence guarantee represented by the data diode would no longer be real.
## 4 Experimental Results and Analysis
### Runtime and Parameter Analysis  
To measure the runtime, the proposed algorithm was implemented on an Inter(R) Core i7 3.4 GHz CPU, with 48 GB RAM, and an NVIDIA GTX 1080 Ti GPU. Table 8 shows the runtime comparison in inference for the conventional methods and the proposed algorithm for an image pair (256 \(\times\) 256). The proposed method incurs 77% and 39% runtime increases against U-Net and ATTUNet in milliseconds, respectively. It is caused by the additional layers of DAG and by employing a dual-path network instead of a single-path network.  
Regarding model parameters, the proposed algorithm has more parameters than conventional algorithms, because it employs the additional layer of DAG for each level over the baseline ATTUNet as shown in Table 9. However, this work presents a novel method to handle reversible input images while preserving the detection rate regardless of the order of the input image pair for change detection.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Architecture** & \(\mathbf{F_{1}}\)**-Score** & **KC** \\ \hline JF + UNet & 0.15 & 10.98 \\ JF + ATTUNet & 0.15 & 10.51 \\ Modified-UNet++ & 0.16 & 11.44 \\ Proposed & 0.7 & 68.74 \\ \hline \hline \end{tabular}
\end{table}
Table 7: \(F_{1}\)-score and KC with reverse image order for the CDD dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**U-Net** & **ATTUNet** & **Modified-UNet++** & **Proposed** \\ \hline
22 & 28 & 35 & 39 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Inference runtime comparison (unit: ms).  
Figure 20: Subjective evaluation of the proposed and conventional algorithms for CDD dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**U-Net** & **ATTUNet** & **Modified-UNet++** & **Proposed** \\ \hline
34,531,073 & 34,882,605 & 36,189,317 & 47,707,149 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Number of parameters for each architecture.
## 10 Conclusions  
The proposed study has given an efficient approach to simultaneously synchronize and localize the underwater sensors that consumed the energy as low as possible. This is a low complexity five-phase algorithm for underwater sensor communication. The proposed methodology is based on a ray-tracing approach which helps in compensation of the stratification effect. The designed model is a one-way message exchange procedure and [PERSON]'s law has been used to derive the relation for variation of sound speed which compensates the stratification effect. The design algorithm localized and synchronized a single ordinary sensor node with low energy consumption. The calculation of the Cramer-Rao lower bound (CRLB) proves the convergence of the proposed methodology. Error analysis has been given to prove the accuracy through simulation. CRLB has theoretically proved the accuracy of the proposed algorithm. Additionally, the computational complexity will increase with the number of observations.  
Experimental results have proved that the provided algorithm outperforms the other previously developed results, our developed model has been compared in terms of several numbers of reference nodes with clock skew error, and offset error, also we have made the comparison between the number of reference nodes and localization an error (RMS). The results of our simulations show that the localization errors, clock skew and offset errors are more stable with the increase in number of reference nodes and number of messages. Energy efficiency has been compared with an error tolerance of  
Figure 16: RMSE of clock offset estimates vs. \(1/o^{2}\).  
Figure 15: RMSE of clock skew estimates vs. \(1/o^{2}\).  
different previous algorithms along with the proposed algorithm. All the presented results have proved that the provided methodology is sufficient for the localization and synchronization of underwater sensor networks as compared to other algorithms.  
For future work, it may be suggested that the proposed algorithm can be extended for a multi-hop environment by making some assumptions to extend the localization coverage area. To the best of our knowledge and according to the literature survey, a joint procedure of localization and time synchronization for the multi-hop environment has not been given yet. Rather, they have been considered separately. Also, we have used a maximum of eight reference nodes, which can be extended in future work.  
The First author ([PERSON]) has developed the methodology, the second author ([PERSON]) has done the formal analysis, the third author ([PERSON]) has made the contribution of supervision, and the fourth author ([PERSON]) has done the written work of manuscript. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
# Intra-Reliability of a Wearable Near-Infrared Sensor for Monitoring the Intensity of Exercise
## 1 Introduction
In those studies, intra-rater reliability was investigated using various statistical approaches, including the intraclass correlation coefficient (ICC) [11; 12; 13; 14; 15], the standard error of measurement (SEM) [11; 14], the coefficient of variation (CV) [11; 13; 14; 15], mean detectable change (MDC) [14], Bland-Altman plots [11; 15], and limits of agreement [12; 15]. The majority of them evaluated SmO\({}_{2}\)/TSI during incremental exercise tests [12; 13; 14; 15]. In previous studies, the reliability of SmO\({}_{2}\) results derived from portable NIRS devices has been studied for Human Hex [11], Moxy Monitor [12; 13; 14], and Portamon [15], but not for Q-LAC. Moreover, available NIRS devices have different frequencies of collecting data, e.g., for Moxy it is 1 Hz [12; 13; 14]. In turn, the new portable device named Q-LAC is characterized by a higher frequency equal to 8 Hz, which is a somewhat lower than Portamon, with a 10 Hz frequency [15]. Therefore, it seems that devices with higher frequency of data collection will be favorable to regular testing of physiological responses during exercise. Considering these arguments, the assessment of reliability using the ICC [15; 16] and sensitivity (ensuring the standard error of measurement is below the smallest worthwhile change) [17] is crucial for examining new tools to validly interpret acute and long-term responses in performance. Hence, the primary objective of this study was to determine the intra-reliability of the portable Q-LAC NIRS device during exercise with an incremental workload. Our hypothesis posited that the Q-LAC measurements would exhibit strong reliability between trials.
## 3 Results and Discussion
### Influence of Excitation
This means that RCP excitation at 532 nm enhanced the emission of the dipoles situated in the middle of the nanohole. LCP excitation, on the contrary, increased the contribution of the dipoles on the rims of the nanohole. Therefore, the resulting emitted field shows a dramatic change in distribution for the excitation handedness change, as seen in Figure 10b. CD\({}_{\text{em}}\) can be enhanced and directed differently in the far-field by changing the polarization of the excitation. The rich chiral behavior of ENHAs can be used to optimize the desired far-field properties and chirality for a specific application in many ways. Take as an example an ENHA coupled with molecules which emit at 940 nm. If one needs high CD\({}_{\text{em}}\) emitted directly to the far-field center (\(\theta_{\text{em}}\)\(\sim\)0\({}^{\circ}\), \(\Psi_{\text{em}}\)\(-\)0\({}^{\circ}\)), the excitation can be tuned to enhance the dipoles on the upper left and lower right positions in the unit cell, as seen in Figure 5. Another requirement can be a high contrast ratio between the CD\({}_{\text{em}}\) with excitation switching from RCP to LCP. As an example, the far-field CD\({}_{\text{em}}\) distributions in Figure 9b show that this is possible for light directed in \(\theta_{\text{em}}\)\(\sim\)10\({}^{\circ}\) cone, and it can be further optimized. Therefore, proper engineering of the field confinement at the excitation wavelength can enhance the contribution of the dipoles which give the highest CD\({}_{\text{em}}\) of the same sign, while the change to their polarization can tune the desired property in the emitted far-field. Moreover, the position of the ensemble in the z-direction can be matched with the high chiral properties. Finally, excitation angle of incidence plays can be used to couple the incident excitation light to the resonance of the nanostructures [41; 43; 45; 46]; e.g., in Figure 8a, this would lead to higher intensity of the emitted light and to an additional degree of freedom in tuning the resulting CD\({}_{\text{em}}\) (Figure 9, left). Finally, the optimization of the emission chiral properties by the excitation of the polarization state, wavelength, and direction will be the subjects of future work.
## 3 Results  
### Validity  
Table 1 displays the relative and absolute validity of the Enode. Passing-Bablok regression indicated fixed (yT, x2, xPBH, and xLoop) and proportional bias (xT, xR, xCatch, and PF) was present in some of the variables. A correction for the differences between the Enode and criterion can be applied using the regression formula, using the intercept and slope provided in Table 1 (8), where X stands for the specific parameter (e.g., yT):  
\[\text{Enode(X)\_corrected}=\text{Intercept(X)}+\text{Enode(X)}\times\text{ Slope(X)}. \tag{8}\]  
All other variables displayed no fixed or proportional bias. Relative reliability displayed 95% confidence ranges from poor to near perfect (Figure 4). The residual standard deviation percentage displayed the greatest measurement errors within horizontal displacement variables. Collectively, this suggests that the Enode is a valid tool in the measurement of some, but not all, biomechanical measures of the barbell during the snatch.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Phase**} & \multirow{2}{*}{**Variable**} & \multirow{2}{*}{
\begin{tabular}{c} **Enode** \\ **Mean**\(\pm\)** SD [95\% CI] \\ \end{tabular} } & \multicolumn{2}{c}{**3D Criterion**} & \multicolumn{2}{c}{**Intercept**} & \multicolumn{2}{c}{**Slope**} & \multicolumn{2}{c}{**RSD**} & \multicolumn{2}{c}{**RSD**} \\  & & **Mean**\(\pm\)** SD [95\% CI] & **Mean**\(\pm\)** SD [95\% CI] & **[95\% CI] & **[95\% CI]** & **[95\% CI]** & **[95\% CI]** & **[9\% CI]** \\ \hline \multirow{3}{*}{1 st pull} & v1 & \(1.06\pm 0.18\) [0.61–1.52] & \(1.15\pm 0.2\) [0.66–1.65] & \(-0.01\) [\(-0.2\)–0.17] & 1.1 [0.94–1.29] & 0.04 [\(-0.08\)–0.08] & 4\% \\  & x1 & \(1.94\pm 1.65\) [1.11–2.76] & \(1.04\pm 1.52\) [0.6–1.49] & \(-0.45\) [\(-1.98\)–0.06] & 0.94 [0.64–1.73] & 0.08 [\(-1.73\)–1.73] & 4\% \\  & y1 & \(25.96\pm 4.27\) [1.48–36.76] & \(30.95\pm 4.44\) [17.7–4.18] & \(1.01\) [\(-380.4-16.61\)] & 1.17 [0.6–2.71] & \(2.48\) [\(-48.46\)–2.86] & 10\% \\ \hline \multirow{3}{*}{Transition} & vT & \(1.51\pm 0.16\) [0.86–2.15] & \(1.51\pm 0.13\) [0.87–2.16] & 0.23 [0.5–0.51] & 0.86 [0.67–1.0] & 0.03 (\(-0.05\)–0.05) & 2\% \\  & **xT** & \(5.35\pm **3.34\) [0.77–5.7] & \(3.68\pm 12.18\) [1.21–2.56] & **0.75 [\(-1.67\)–1.67]** & **0.63 [\(-0.39\)–1.92]** & **1.22 [\(-2.04\)–0.20]** & **23\%** \\  & **yT** & \(5.35\pm **6.52\) [2.88–17.85] & \(5.18\pm 0.35\) [0.99–22.78] & **0.27 [\(-2.68\)–2.63]** & **0.77 [\(-0.85\)–1.1]** & **1.61 [\(-3.35\)–1.35]** & 4\% \\ \cline{1-1}  & vLoss & \(0.44\pm 0.18\) [0.25–0.63] & \(0.36\pm 0.15\) [0.21–0.51] & \(-0.07\) [\(-0.21\)–0.10] & 0.93 [0.55–1.31] & 0.07 (\(-0.14\)–0.14) & 4\% \\ \hline \end{tabular}
\end{table}
Table 1: Between-unit comparison of the Enode and criterion using the average of session 1 and 2
# Robust Symbol Timing Synchronization for Initial Access under LEO Satellite Channel
###### Abstract  
This paper proposes a robust symbol timing synchronization scheme for return link initial access based on the Digital Video Broadcasting-Return Channel via Satellite 2 nd generation (DVB-RCS2) system for the Low Earth Orbit (LEO) satellite channel. In most cases, the feedforward estimator structure is considered for implementing Time Division Multiple Access (TDMA) packet demodulators such as the DVB-RCS2 system. More specifically, the Non-Data-Aided (NDA) approach, without using any kind of preamble, pilot, and postamble symbols, is applicable for fine symbol timing synchronization. However, it hinders the improvement in estimation accuracy, especially when dealing with short packet lengths during the initial access from the User Terminal (UT) to the Gateway (GW). Moreover, when a UT sends a short random access packet for initial access or resource request to the LEO satellite channel, the conventional schemes suffer from a large Doppler error depending on UT's location in a beam and satellite velocity. To ameliorate these problems, we propose a novel symbol timing synchronization algorithm for GW, and its advantage is confirmed through computer simulation.  
DVB-RCS2; LEO satellite; demodulator; Doppler; symbol timing recovery (STR); Non-Terrestrial Network (NTN); _FFT_ (Fast Fourier Transform) +
Footnote †: journal: Sensors