esa-sceva/satcom-chunk-collection huggingface dataset:
# Modeling and Profiling of Aggregated Industrial Network Traffic
## 1 Introduction
### Related Work  
Several recent studies tried to tackle the challenges associated with brownfield traffic from various angles. Flow-based modeling is proposed in [13] to profile communication patterns in industrial IP networks for intrusion detection. However, the flow-based modeling proved unpractical because of the complex communication patterns. In [14] inter-arrival time and correlation models were defined as additional key parameters for traffic characterization and flow-based modeling in SCADA networks. The method showed promising results for anomaly detection on lab-generated data [15], but applying on real traffic, the performance was unsatisfactory [16]. Four on-off-based models were proposed in [8] to model the communication flows. The application of this approach was discussed in relation to different traffic classes in industrial networks.  
In [9] a methodology is proposed for evaluating the availability of resources for new traffic integration for network configuration management. Inter-arrival time and packet size were the measured parameters for flow-based resource estimation.  
A case study [10] highlighted the disparity between the common characteristics and modeling assumptions based on traffic classes and those seen in the data collected from brownfield with aggregated traffic classes. Ref. [17] further emphasized this finding by experimental evidence from three case studies for 5G systems.  
Addressing the complexity of provisioning and configuration of the scaled-up IT-OT networks, and inaccurate results from the commonly applied traffic measurement for characterization and class-based model assumptions, a new measurement with the scope on network total traffic is introduced in [10]. The measurement applied on an aggregated traffic showed the mirroring bandwidth consumption patterns related to the operational work-cycles.
## 4 Discussions  
### Influence of Thickness on Water Absorption  
The measured water absorption was analyzed against specimen thickness at specific ageing days (Figures 9 and 10). Figure 9a,b show that water absorption of the specimens decreased by increasing specimen thickness in the early stage (less than two days) of the immersion in deionized water and alkaline solution, respectively. In other words, the thinner the specimen thickness, the faster the water absorption saturation was achieved. As shown in Figure 9c,d, when the ageing time increased to four and seven days, the water absorption rose and then declined as the increasing specimen thickness. This is because in this ageing stage the change in the weight of specimen B\({}_{1}\) began to be dominated by the hydrolysis of the epoxy resin (reducing the weight of the specimen) rather than water absorption (increasing the weight of the specimen). While the changes in the weight of specimen B\({}_{2}\) and B\({}_{3}\) were still dominated by water absorption. At this time, the mass gain of specimen B\({}_{1}\) from water absorption was less than the mass loss of epoxy resin due to the hydrolysis, leading to a decrease of measured water absorption of specimen B\({}_{1}\). As shown in Figure 10, the water absorption trend in late stage of the immersion (14-180 days) was opposite to that in early stage, indicating that the mass loss of all specimens due to the hydrolysis of epoxy resin was more than the mass gain of water absorption in this stage. The mass loss of thinner specimen (B\({}_{1}\)) was still greater than that of thicker specimens (B\({}_{2}\), B\({}_{4}\)) in late stage, meaning that a thinner specimen had a greater hydrolysis degree at the given ageing time.  
It is noteworthy that the water absorption of BFRP laminates was studied by considering the epoxy resin and the basalt fiber as a whole in this study. It is significant to determine water absorptions of the resin and basalt fiber respectively for a better understanding of the water absorption mechanism. In a future study, the related work (i.e., separately studying the water absorption of the resin and the basalt fiber) will be incorporated in the authors' study target.  
Figure 8: SEM images of tensile failure surfaces: (**a**) unaged specimen; aged specimen soaked in 60 \({}^{\circ}\)C alkaline solution last (**b**) 14 days and (**c**) 180 days; (**d**) unaged basalt fiber; aged basalt fiber soaked in 60 \({}^{\circ}\)C (**e**) deionized water and (**f**) alkaline solution last 180 days.
## 3 Results
### Clinical Assessment  
The average values recorded from VAS for pain evaluation decreased from 6.33 \(\pm\) 1.40 points at the first 2 weeks follow-up, to 1 \(\pm\) 0.79 at 1 year (P \(<\) 0.0001). Detailed data about average values from VAS in Table 2. At 1-year post-surgery mean ROM was 124 \(\pm\) 6\({}^{\circ}\). Overall, all patients showed a symmetrical performances recovery.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Sex**} & Male & 18 (53\%) \\ \cline{2-3}  & Female & 16 (47\%) \\ \hline
**Age** & & 57.5 \(\pm\) 12.782 \\ \hline
**Height (m)** & & 1.71 \(\pm\) 0.10 \\ \hline
**Weight (kg)** & & 71.17 \(\pm\) 7.78 \\ \hline
**BMI (kg/m2)** & & 24.23 \(\pm\) 1.97 \\ \hline \multirow{2}{*}{**Schatzker Classification [3]**} & Type II & 18 (52.9\%) \\ \cline{2-3}  & Type III & 6 (17.6\%) \\ \cline{2-3}  & Type VI & 10 (29.4\%) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Demographics.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multirow{2}{*}{**VAS**} & 2 weeks post-op & 6.33 \(\pm\) 1.40 \\ \cline{2-3}  & 1 month post-op & 5.2 \(\pm\) 0.89 \\ \cline{2-3}  & 3 months post-op & 3.1 \(\pm\) 0.71 \\ \cline{2-3}  & 6 months post-op & 1.43 \(\pm\) 0.69 \\ \cline{2-3}  & 1 year post-op & 1 \(\pm\) 0.79 \\ \cline{2-3}  & Percentage decrease & 84.20\% \\ \hline \multirow{4}{*}{**SF-36**[23]} & Limitations in physical activities & 92.5 \(\pm\) 11.65 \\ \cline{2-3}  & Limitations in social activities & 96.67 \(\pm\) 6.23 \\ \cline{2-3}  & Limitations in usual role activities & 96.83 \(\pm\) 3.34 \\ \cline{2-3}  & because of physical health & 91.8 \(\pm\) 9.31 \\ \cline{2-3}  & Bodily pain & 91.8 \(\pm\) 9.31 \\ \cline{2-3}  & General mental health & 86.93 \(\pm\) 3.78 \\ \cline{2-3}  & Limitations in usual role activities & 94.33 \(\pm\) 12.89 \\ \cline{2-3}  & Because of emotional problems & 87.5 \(\pm\) 6.79 \\ \hline \multicolumn{2}{c}{Vitality} & 87.5 \(\pm\) 6.79 \\ \cline{2-3}  & General health perception & 93.6 \(\pm\) 4.90 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mean visual analogue scale (VAS) and Short Form (36) Health Survey (SF-36) values.  
None of the 34 patients who participated the study reported vascular or nervous deficits. Moreover, we did not observe any clinically evident infectious complication.  
At 1 year, the average score obtained from Tegner Lysholm Knee Scoring Scale was \(89\pm 4.10\); the average score derived from IRDC 2000 questionnaire was \(78.67\pm 3.31\).  
The average values of each scale of the SF-36 are reported in Table 2.
## 3 Experimental Analysis
### Load Decomposition  
STL is used to decompose the 8760 load data samples. Figure 4 shows the load data samples for the first two weeks (336 h) and their STL results. From Figure 4, it is observedthat the original load sequence, after STL, is decomposed into a relatively smooth-changing trend component, a seasonal component with a periodicity of approximately 24 h, and a residual component with significant fluctuations.
# Mobility-Enabled Edge Server Selection for Multi-User Composite Services
## 2 Motivation Scenarios
### Multi-User Edge Server Selection with Composite Services  
Figures 2 and 3 illustrate a more complicated example of multi-user edge server selection regarding service composition in the mobile edge computing environment.  
In the real world, users always invoke a series of services when they move [16]. As shown in Figure 2, three mobile users would like different composite services in the mobile path. The number in the upper left corner of each service icon indicates the size of the upload data. The number in the upper right corner of each service icon indicates the download data size. The number below each service icon indicates the response time of the service. Suppose that the information of all services, including the upload data size, download data size, the average service response time, and so on, is predetermined. We assume that the workload of each service is two units and the total computing capacity of each edge server is four units. The service downtime generated by the service migration time is set as 2 s [15].  
The problem is how to select edge servers for mobile users to minimize the total waiting time. The traditional method tries to select the edge server with the most bandwidth when the user wants to invoke the service. The method only minimizes the single-user latency of uploading input data and downloading output data. The data transmission time of the follow-up users cannot be guaranteed due to the limited resources of each edge server and the mobility of the user. As shown in Figure 3a, the total waiting time of all services is 1666 s using the traditional method. However, if the user's mobility and the limited resources of each edge server are taken into consideration when selecting the edge server, a better edge server selection result can be obtained, and the users' waiting time can be reduced. The total waiting time of the all services is 1630.5 s according to the selection process shown in Figure 3b.  
Hence, it is important to consider users' mobility and the limited resources of an edge server when selecting edge servers for composite services in order to reduce users' waiting time. The process in which the former user selects edge server may affect the follow-up user's edge server selection, which could affect the total waiting time. Therefore, we need to find a method that selects edge servers for mobile users in advance to minimize users' total waiting time.  
Figure 2: Examples of service composition.
# Positron Emission Tomography in Coronary Heart Disease
### Imaging Inflammation with 18F-Fluorodeoxyglucose (FDG)  
FDG is overall the most used radioligand in PET imaging. It is a glucose analog that enters cells via facilitated glucose transporter member (GLUT) 1 and 3 and undergoes phosphorylation to become (18)F-FDG-6-phosphate, which cannot exit the cell before radioactive decay. Therefore, signal intensity correlates with cellular glucose uptake and phosphorylation, which indicates high metabolic activity. In the case of the atherosclerotic plaque, metabolic activity appears to be related to the concentration of proinflammatory macrophages, as demonstrated in animal models [7]. In humans, serum levels of myeloperoxidase were associated with carotid plaque FDG uptake [8].  
Aortic FDG uptake was independently associated with cardiovascular risk factors such as increased low-density lipoprotein and total cholesterol, and with the presence of metabolic syndrome [9; 10; 11]. On average, patients with myocardial infarction (MI) were shown to have higher aortic FDG uptake compared with stable angina patients [12]. Visceral adipose tissue uptake of FDG was associated with systemic inflammatory status, and with the presence of metabolic syndrome components [13]. Additionally, it was independently associated with the severity of CAD and with the occurrence of AMI [14].  
Regarding the assessment of pharmacological treatment, statins were shown to reduce the arterial FDG signal in a dose-dependent manner and pioglitazone was shown to attenuate vascular FDG uptake [15; 16; 17]. In contrast, the novel antidyslipidemia drugs Dalcetrapib (cholesterol ester transfer protein inhibitor) and Rilapladib (lipoprotein-associated phospholipase A2 inhibitor) did not have any effect on vascular FDG activity [18; 19].  
There is, however, no current evidence to support the implementation of FDG-PET for atherosclerotic risk stratification in clinical practice.
# A Dermoscopic Inspired System for Localization and Malignancy Classification of Melanocytic Lesions
## 3 Results
### Evaluation of Hair Detection and Lesion Segmentation: Results  
Hair detection and exclusion is performed prior to lesion segmentation to eliminate the artifacts thereby increasing the accuracy of segmentation. A positive effect of pre-processing (hair detection + black frame removal) on the segmentation accuracy for the combined dataset can be observed from the Figure 9. The overlap error after applying hair detection algorithm prior to segmentation was 0.07, and the overlap error obtained without applying hair detection algorithm prior to segmentation was 0.15. This proves that hair detection improved the accuracy of segmentation.  
The proposed segmentation method resulted in sensitivity, specificity, accuracy and overlap error of 92.5%, 96.7%, 95.7%, and 8.2%, respectively, for the combined datasets. The overlap segmentation error for modified Chan-Vese (proposed method) and Chan-Vese for combined dataset is illustrated in Figure 10. The Overlap Error (OE) is calculated as given in (11).  
\[OE=\frac{Area(G\oplus S)}{Area\ (G)} \tag{11}\]  
Figure 9: Effect of pre-processing on segmentation accuracy.  
where \(G\) is the ground truth and \(S\) is the segmented binary image.
## 3 Results  
### Characteristics of the Sample  
The sample consisted of 93 workers, with 18 excluded due to invalid accelerometry data, leaving 75 vineyard workers for analysis. Among them, 65.3% (\(n\) = 49) were female, 65.3% (\(n\) = 49) worked in the field, and the average age was 28.9 (\(\pm\)8.9) years. The average BMI was 26.3 (\(\pm\)5.1) kg/m\({}^{2}\), 95.9% (\(n\) = 71) were non-smokers, and 51.4% were alcohol consumers. Descriptive characteristics are shown in Table 1.  
Regarding pain, the lower back had the highest average pain intensity (4.64 \(\pm\) 3.9) in the past three months, especially among packing workers (5 \(\pm\) 3.9). The upper back had the second highest pain intensity (4 \(\pm\) 3.9), and the neck had the lowest (3.3 \(\pm\) 3.8). When pain was categorized as \"Low\" and \"High\" intensity, the prevalence of high-intensity pain was 46.7% for the neck, 52% for the upper back, and 60% for the lower back.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Variable** & **Total Sample** & **Field** & **Packing** \\ \hline Age (years)–M (SD) & 28.9 (\(\pm\)8.9) & 30 (\(\pm\)9.7) & 27.4 (\(\pm\)1.3) \\ Sex & - & - & - \\ Female–\(n\) (\%) & 49 (65.3\%) & 28 (37.3\%) & 21 (28\%) \\ Male–\(n\) (\%) & 26 (34.7\%) & 21 (28\%) & 5 (6.7\%) \\ BMI (kg/m\({}^{2}\))–M (SD) & 26.3 (\(\pm\)5.1) & - & - \\ Underweight–\(n\) (\%) & 3 (4\%) & 1 (1.3\%) & 2 (2.7\%) \\ Normal–\(n\) (\%) & 28 (37.3\%) & 20 (26.7\%) & 8 (10.6\%) \\ Overweight–\(n\) (\%) & 44 (58.7\%) & 28 (37.3\%) & 16 (21.4\%) \\ Function & - & - & - \\ Field–\(n\) (\%) & 49 (65.3\%) & - & - \\ Packing–\(n\) (\%) & 26 (34.7\%) & - & - \\ Non-smoker–\(n\) (\%) & 71 (95.9\%) & 45 (60.8\%) & 26 (35.1\%) \\ Alcoholic consumer–\(n\) (\%) & 38 (51.4\%) & 25 (33.8\%) & 13 (17.6\%) \\ Pain Intensity (0–10) & - & - & - \\ Neck–M (SD) & 3.28 (\(\pm\)3.7) & 3.3 (\(\pm\)3.8) & 3.32 (\(\pm\)3.7) \\ Upper back–M (SD) & 4.24 (\(\pm\)3.9) & 4 (\(\pm\)3.9) & 5 (\(\pm\)3.9) \\ Lower back–M (SD) & 4.64 (\(\pm\)3.9) & 4.3 (\(\pm\)3.9) & 5.2 (\(\pm\)3.8) \\ \hline \hline \end{tabular}  
* M: Mean; SD: Standard Deviation; BMI: Body Mass Index.  
\end{table}
Table 1: Demographic Characteristics of the Sample.
# The Method of Production Scheduling with Uncertainties Using the Ants Colony Optimisation
## 2 Production and Maintenance Scheduling Model
* Probability \(P\) that in the interval \([f,g]\in[mT,(m+1)T)\) there occurs at least one failure,
* Period of increased risk of failure \([a,b+MTTR]\), where: \(a\) on the assumption that the probability of the failure-free time of the bottleneck is higher than \(a\) equalling 30%, \(b\) on the assumption that the probability of the failure-free time of the bottleneck is less than \(b\) equalling 70%. In the fifth stage, the predictive schedule is generated for the reliability characteristics using the ant colony optimisation algorithm. The procedure of generating predictive schedules is presented in Section 4. The stability of schedule \(k\) is measured using the quality robustness and solution robustness criteria. The reactive schedule \(k^{*}\) is generated in a situation where the predictive schedule \(k\) can not absorb the impact of the disturbance. The newly generated schedule should reproduce the previous one as much as possible according to the stability criterion:  
\[SR(k*)=\sum_{j=1}^{J}\sum_{v_{j}=1}^{V_{j}}\big{|}st_{j,v_{j}}(k)-st_{j,v_{j} }(k*)\big{|}, \tag{6}\]  
where: \(st_{j,v_{j}}(k)\) is start time of operation \(v_{j}\) of task \(j\) in predictive schedule \(k\); \(st_{j,v_{j}}(k*)\) is start time of operation \(v_{j}\) of task \(j\) in reactive schedule \(k^{*}\). After the disturbance, the value of the criterion used to evaluate the predictive schedule should not be significantly influenced. The quality robustness of schedule \(k\) is assessed by calculating the difference between the makespan criterion \(C\) before and after the machine failure:  
\[QR(k*)=|C(k)-C(k*)|. \tag{7}\]
## 2 Materials and Methods
### Data Collection and Analysis  
Data was collected for 24 h, with the QKD system operating continuously. The data were analyzed using the Zabbix monitoring software 6.4.15 [10], which provided the real-time monitoring and visualization of the key performance metrics implemented using Grafana [11]. Two Fortinet FortiGate firewalls were used to set up an IPSec tunnel where QKD-generated keys were used for encryption. Two end hosts were connected on the LAN side of FortiGate firewalls, and iPerf3 was used to continuously generate traffic from [PERSON] to [PERSON] for monitoring purposes to determine the stability of the encrypted channel. Figure 1 shows the setup used for the experiment, including hardware and software. Two Cerberis3 QKD nodes with default configuration were used, two Fortinet Fortigate VM firewalls wereused for QKD-enabled IPsec tunnel termination, and the Zabbix 6.4.15 server version was used to monitor QKD nodes over the SNMPv3 protocol.
## 6 Performance Evaluation
### Evaluation for the Analytical Model
#### 6.2.7 One-Hop Transmission Distance for the Two-Lane Scenario  
Figure 11 depicts the PMF of one-hop transmission distance for the two-lane scenario with different vehicular traffic density. Once again, we can find the accuracy of our analytical model on describing the distribution of the one-hop transmission distance in the two-lane scenario. With the defined values of default parameters, we can see that the one-hop transmission distance tends to approach the radio range \(R\). With the increase of the vehicular traffic density, the PMF shape becomes more compact.  
Figure 10: The PMF of one-hop timer delay for the two-lane scenario with different vehicular traffic density, where \(v_{\Delta}=20\) km/h. (**a**) \(\rho_{1}=10\), \(\rho_{2}=5\) vehs/km; (**b**) \(\rho_{1}=50\), \(\rho_{2}=25\) vehs/km; (**c**) \(\rho_{1}=100\), \(\rho_{2}=50\) vehs/km.  
Figure 9: The PMF of one-hop transmission distance for the inter-lane scenario with different vehicular traffic density: (**a**) \(\rho_{2}=10\) vehs/km; (**b**) \(\rho_{2}=50\) vehs/km; (**c**) \(\rho_{2}=100\) vehs/km.
## 4 Results
### Models' Comparative Analysis
#### 4.4.1 Evaluation of Predictive Interval  
A comparison of the best models using PICP, PINAW, and PINAD is shown in Table 6. The PINC is taken to be a 95 percent level of confidence, where it is valid for all the models. All the models have valid PICPs. They are all greater than 95%. A model with the narrowest PINAW and smallest PINAD is recognised to be the best fitting model [50]. In this case, the best fitting model is RNN.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variables** & **Coefficients** \\ \hline Intercept & \(-2.158294\times 10^{4}\) \\ Temp & \(3.815809\times 10^{1}\) \\ RH & \(-1.326136\times 10^{0}\) \\ WD & \(1.645751\times 10^{-1}\) \\ Rain & \(0.00000\) \\ WS & \(1.866991\times 10^{0}\) \\ WD StdDev & \(7.173793\times 10^{0}\) \\ BP & \(2.215658\times 10^{1}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parametric coefficients.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **GA** & **RNN** & **KNN** \\ \hline RMSE & 35.50 & 56.89 & 57.48 \\ rRMSE & 5.96 & 7.54 & 7.58 \\ MAE & 26.74 & 20.18 & 20.94 \\ rMAE & 5.17 & 4.49 & 4.58 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Assessment of models.  
Figure 4: Plot of GHI superimposed with forecasts from RNN, GA, and KNN models.  
Figure 5: Density plots of the actual GHI (solid lines) and model’s forecasts (dashed lines) where **Top left panel**: Actual GHI and forecasts of GA **Top right panel**: Actual GHI and forecasts of RNN **Bottom left panel**: Actual GHI and forecasts of KNN and **Bottom right panel**: Actual GHI and forecasts of RNN, GA, and KNN where the actual GHI and forecasts of GA are shown by solid lines.
# Finite Element Modeling of the Soil-Nailing Process in Nailed-Soil Slopes
## 4 Finite Element Modeling Results and Discussion  
The developed finite element (FE) models of the soil-nailing process were used to obtain horizontal/vertical displacement and horizontal/vertical stresses at different locations of nailed-soil slope under various stages, i.e., construction stages and overburden pressure stages, using the commercial software PLAXIS. Deformed shapes of slope were also obtained at the different soil-nailing process stages. The medium dense sand with relative density of 48% was used in the development of FE models. However, the effect of density was studied considering three relative densities of sand, i.e., 34%, 48% and 68%.
# An Accurate Probabilistic Model for TVWS Identification
## 4 Proposed Probabilistic Model  
This section provides a brief explanation of the probabilistic model developed to address the research objectives. We start by analyzing how to calculate the reference noise level. Then a brief review of \(M\) and \(T\) proposed by other authors, together with the parameters considered to calculate \(T\), are detailed. The normality of the used distributions and the applicable tests are also included. Then, attention is paid to obtaining the pdf from the collected data, the used performance metrics, how to calculate \(P_{fa}\) and \(P_{d}\), their maximum and minimum values, and how these values are related among them.
## 6 Results and Discussions
### S-Parameters (S11) and Impedance (Z11)  
Figure 17 illustrates the simulated and observed S11 curves of the UWB and dual-band UWB. It is evident that the projected UWB antenna structures have a measured (Keysight N9951A VNA is used) impedance bandwidth of approximately 113% between the range of 3.7 and 13.3 GHz. Conversely, the first notch is achieved between the frequencies of 5 and 5.6 GHz, filtering the IEEE 802.11, Wi-Fi, and WLAN bands. The second notch is achieved between the frequencies of 7.3 and 8.3 GHz, covering the complete X-band satellite communication. The measured results of the UWB band notches are in line with the simulation.  
The impedance parameter (Z11) assists in identifying the operating frequency of the antenna element, which has only a resistive part and no reactive portion. The impedance curve of the projected UWB has a resistive part approximately equal to the 50 \(\Omega\) and nearly 0 reactive components, as depicted in Figure 18a. Similarly, the dual-band notch antenna has nearly 50 \(\Omega\) resistive parts and zero reactive components, respectively, except at the notched frequency bands. At the dual notch frequencies, the impedance has (98.25 \(-\) j22.89) \(\Omega\) and (141.19 \(+\) j40.46) \(\Omega\), correspondingly, as depicted in Figure 18b.  
Figure 15: The CMA of the proposed UWB antenna at the center frequency: (**a**) MS, (**b**) CA, and (**c**) the first four significant modes of the current distribution.  
Figure 16: Prototypes of the proposed antenna designs: (**a**) UWB antenna, and (**b**) UWB band, notch antenna.  
Figure 17: S11 curves of the (**a**) UWB and (**b**) UWB band notch antennas.