esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimentation and Results Analysis
### Dailing with Cold Starts  
In the recommendation model about the cold start problem, its solution is more difficult, which is due to the interaction behavior between the user and the project being less active, which will lead to the model being constructed, for it cannot achieve the optimal representation, so then it is difficult to recommend the project for the user [31; 32; 33]. Through the experiment, 1000 users are randomly selected to use the C-MBR model to carry out a comparative learning task analysis on the different behaviors of a low interaction between users and projects, delete the interactions on the training set, carry out ink training for the rest of the users, and recommend projects for the experimental users, to obtain the results of the experiment. The results show that C-MBR has a strong ability to deal with the problem of sparse data, which is because C-MBR can extract the correlation information of the same site in the construction of the global structure module, and improve the value of site recommendation by evaluating the user's previously interacted sites with the target site using comparative learning, and predicting the final score of the site based on the principle of the similarity phase (see Figure 1). Compared with the ablation experiment, the model performance of C-MBR is significantly enhanced, showing that the C-MBR model can fully apply the information of global structure and local structure in cross-structure comparative learning and multi-behavioral comparative learning to further alleviate the problem of data sparsity.
# Evaluating the High Frequency Behavior of the Modified Grounding Scheme in Wind Farms
## 4 Simulation Results and Discussion
### Single Wind Turbine
#### 4.2.1 Indirect Strike
Figure 7: Circuit of the simulated wind turbine with dependent cable. Impact of increased horizontal conductor size on transient overvoltages: In this case, the horizontal electrodes of the PMGS are increased from their actual length, 9.2 m, to 15 m. Figure 9 indicates that the maximum overvoltages on the LV side of the main power transformer (boost transformer) and the control equipment reach approximately 1 kV, which is in the normal range of the rated voltage. In this case, a nominal amount of overvoltage has again been obtained for the HV side. The outcomes establish that using the PMGS would be beneficial in rough areas with high soil resistivity that are difficult to dig. Impact of increased vertical conductor size on transient overvoltages: In this subsection, the length of the electrical rods of the PMGS is increased from 3 m-4 m to see the probable changes in transient voltages. From Figure 10, it can be seen that increasing the PMGS rod length does not by itself significantly change the transition behavior, since the maximum overvoltages are still around 1.5 kV for the LV side of the boost transformers and the control circuits, while this amount is so small for the HV side of the transformer that it can be ignored. Hence, it has been established that increasing the rod length cannot further improve the protection behavior. Impact of comprehensive wind turbine model with the PMGS on transient overvoltages: The previous experiments show that to accomplish an effective model, it is necessary to integrate the PMGS with a high-frequency SA and FD cable. As has been shown regarding each of these devices, their consolidation can considerably reduce the number of transitions, as illustrated in Figure 11.  
Figure 8: Impact of frequency-dependent cable on transient overvoltages. (**a**) LV side of the main transformer; (**b**) HV side of the main transformer from node to ground; (**c**) control circuits. Figure 10: Impact of increased vertical conductor on transient overvoltages. (**a**) LV side of the main transformer; (**b**) HV side of the main transformer from node to ground; (**c**) control circuits. Figure 9: Impact of increased horizontal conductor on transient overvoltages. (**a**) LV side of the main transformer; (**b**) HV side of the main transformer from node to ground; (**c**) control circuits. Figure 12 shows that the peak voltage value for the LV side reaches 1 kV and for the control circuit reaches nearly 1.5 kV, both of which are below the upper limits the electrical devices can support and therefore ensure their safety. In addition, this value is less for the HV side and can therefore be neglected.
# An Optical Differential Method for Underwater Wireless Communication in Turbid Environments
###### Abstract  
Underwater optical communication has emerged as an essential tool for exploring oceanography and marine resources for underwater vehicles or robots in recent years. Current techniques mostly rely on the paradigm of intensity modulation and direct detection, resorting to more powerful light sources on the transmitting side and more sensitive detectors on the receiving side, thus causing excess energy consumption and system costs. Here, a novel approach, namely, the optical differential communications method (ODCM), is proposed to extend the distance of underwater wireless optical communications in turbid water. The underlying physical reason is explained in theory and demonstrated in experiments. It is found that the stable propagation distance of ODCM could be further extended without relying on intensive light sources, in contrast to conventional methods, showing potential for longer communication ranges. Tests of underwater optical communications are conducted, and the results show that ODCM can significantly reduce the bit error rate (BER) at the same propagation distance or extend the propagation distance for the same BER level of optical signals. As such, this study provides an avenue for long-distance and stable underwater wireless optical communications in turbid environments.  
underwater optical communications; turbid environment; optical differential method; chromatics Article
## 3 Results
### Model Optimization and Validation  
In this study, the Biome-BGC model was used to simulate the NEE from March 2010 to February 2011, and 19 parameters--including SLA and C: Nleaf--were optimized using the actual NEE and the PEST method. The results showed that the simulation performance of the model optimized by the PEST method was improved (Figure 3a). Before calibration, the original model had a certain degree of deviation (R\({}^{2}\) = 0.53, RMSE = 1.62); it had a significant underestimate of NEE from August to October and an overestimate from March to May. The revised model was significantly improved (R\({}^{2}\) = 0.72, RMSE = 1.14). The overall variation in the simulated values was reduced, and the trend was more obvious. The overall relative error of the simulation improved from 16% to 13%, but there was still a degree of underestimation.  
Based on the sensitivity analysis results presented in Section 3.1, we selected several parameters with strong sensitivity from the 19 parameters--namely, SLA, k, C: Nleaf, FLNR, SLAshd: sun, FRC: LC, and SC: LC--and used the PEST method to optimize only these 7 parameters. We named the scheme optimized with 19 parameters \"Scheme 1\" and the scheme optimized with 7 parameters \"Scheme 2\". The results showed that Scheme 2 could also achieve accurate optimization results (R\({}^{2}\) = 0.71, RMSE = 1.19), and the difference between the simulation results of the two optimization schemes was small (Figure 3b).  
Figure 1: First-order sensitivity index (S\({}_{\rm{i}}\)) and total sensitivity index (S\({}_{\rm{II}}\)) of key parameters during 2010–2020 with different outputs: (**a**) with NEE as the output; (**b**) with GPP as the output; (**c**) with LAI as the output.  
Figure 2: (**a**) First-order sensitivity index and (**b**) total sensitivity index in the ranges of \(\pm 10\%\) perturbation and \(\pm 20\%\) perturbation of parameter values.  
Based on the optimized model parameters, we conducted validation on the NEE from March 2011 to December 2020 (Figure 4). The simulation results showed that the model could accurately simulate NEE in other years (R\({}^{2}\) = 0.64, RMSE = 0.98) and the trend of NEE in different phenological periods could be well-described. However, the overall simulation accuracy was lower than that in 2010 due to the lack of eddy covariance observation data in 2016 and other years. The overall relative error of the simulation was 12%, showing an overall underestimation of carbon sink capacity.
# Recent Applications of Artificial Intelligence in Radiotherapy: Where We Are and Beyond
## 3 Results and Discussion
### Reported Application of AI to RT
#### 3.2.4 Synthetic Image Generation  
During RT, multiparametric MR images can allow more comprehensive characterization of the investigated area, while daily pre-therapy CBCT images are widely used for patient positioning verification. Unfortunately, the intensity values of MR [73] or CBCT [74] images are not directly related to the electron density of the tissues, which represents one of the input data issues in treatment planning. Moreover, CBCT is a practical low-dose image modality, but it suffers from poor image quality compared to CT. Thus, there is a need to derive CT-equivalent information (i.e., \"synthetic\" CT images), which is mandatory for RT treatment planning and to generate digitally reconstructed radiography (DRR) images that can be used for patient setup verification. Using this strategy, the generation of synthetic images allows fully MR-based planning, while reducing the mismatch that originates during the transfer of contours from one image modality to another and caused by organ motion and changes [75; 76; 77].  
Figure 5: Numbers of identified papers in image segmentation according to (**a**) the contoured district using ML or DL methods, and (**b**) the imaging modality used for segmentation by adopting the convolutional neural network (CNN) approach.  
Regarding MR images, synthetic CT image generation based on segmentation and atlases has been proposed [78]. Unfortunately, the first approach is time-consuming, requiring the acquisition and segmentation of multiple MR sequences. At the same time, the atlas-based approach relies on DIR methods and depends on the cohort included in the atlas. In this context, DL approaches are considered the most effective strategy due to their ability to learn complex models without acquiring additional images or solving image-registration issues. DL-based \"synthetic\" CT image generation was implemented in 89% (Figure 6a) of papers, while ML was applied in 11% of papers (Figure 6b). Both approaches employed multiple 2D or 3D images as input data.  
Among DL-based methods, several examples were based on CBCT [79,80], MR [73,81,82], or 2D projection [83,84] images. CNN approaches were also adopted [85] to speed up the online setup verification using MR-guided RT.  
Another application of AI methods involves the generation of synthetic images with increased quality, performed mainly on CBCT images (in 11/21 papers). Finally, [PERSON] et al. [39] generated synthetic MR images to improve the auto-segmentation accuracy.
# Dynamic Line Rating--An Effective Method to Increase the Safety of Power Lines
## 3 Possible Solutions to Reduce the Electric Field
### The Application of the Expert System for an Operating Power Line  
An example is given for one span of an active power line equipped with a DLR sensor to present the expert system's applicability and its benefits in comparison to the static line rating. The OHL's geometry and operating parameters are the same as detailed in Section 2 and Figure 1. According to the expert system, the critical span analysis was performed; the results are shown in Figure 7. The lowest clearance is 10.48 m in the span between towers 2 and 3, which should be equipped with a line monitoring sensor.  
The span fulfills the necessary safety distance criterion of 8 m. The minimum clearance from the electric field's peak value is determined to be 10.8 m from the finite-element simulation presented in Figure 8a.  
Based on Figure 8a, the maximum operating temperature should be decreased from 60 \({}^{\circ}\)C to 50 \({}^{\circ}\)C, at which the lowest clearance is 10.84 m to fulfill the electric field criterion. The next step is to calculate the ampacity limit of the line. If the static line rating is applied--differing from the expert system--the worst-case parameters listed in Table 6 need to be used.  
The new static line rating is 1193 ampere at each phase at 50 \({}^{\circ}\)C, which means 25.2% lower transferrable power than at 60 \({}^{\circ}\)C. With DLR in the expert system, this power loss can be prevented. Based on the sensor and weather stations' real-time measurements, the ampacity is always at its theoretical maximum, which means a 20-30% average increase in power for a whole year. Figure 8b illustrates a so-called duration curve for a year that illustrates power lines' capacity utilization. The area under the load duration curve is proportional to the amount of energy that can be transferred via OHLs, while the statistical  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Symbol** & **Weather Parameter** & **Value** & **Measuring Unit** \\ \hline T\({}_{\text{a}}\) & Ambient temperature & 30 & [\({}^{\circ}\)C] \\ w\({}_{\text{s}}\) & Wind speed & 0.61 & [m/s] \\ w\({}_{\text{d}}\) & Wind direction & 90 & [\({}^{\circ}\)] \\ S & Solar radiation & 900 & [W/m\({}^{2}\)] \\ P & Precipitation & 0 & [mm/h] \\ \(\epsilon\) & Emissivity factor & 0.6 & [\(\cdot\)] \\ \hline \hline \end{tabular}  
* These values can vary from territories depending on the prevailing climate.  
\end{table}
Table 6: Weather parameters for static line rating 1.  
Figure 7: The critical span from the aspect of clearance, equipped with DLR sensor.  
curve itself represents what proportion of the year the given ampacity limit can be provided. According to Figure 8b, applying DLR with reduced maximum temperature can result in a higher ampacity limit to the non-reduced temperature case for more than 25% of the year. Thus, with the expert system's help, it is possible to keep the electric field strength below the permissible limit, which increases safety and security and increases the ampacity limit of the transmission line for one year.
## 1 Introduction  
In today's industrial landscape, characterized by rapid technological advancements and rising market demands, manufacturing organizations face increasing pressure to boost productivity, optimize processes, and stay competitive. However, a significant challenge remains: leaders struggle to effectively monitor production and machine parameters without real-time monitoring systems that incorporate Digital Twin technology [1].
# Narrative Review on Methods of Activating Irrigation Liquids for Root Canal Treatment
## 4 Irrigants Activation Techniques
### Ultrasound Techniques  
The application of ultrasounds during and after root canal preparation is a crucial step in enhancing disinfection. The frequencies utilized in dental ultrasound equipment range from 25 to 40 kHz. Disinfection properties of ultrasound are based on the phenomena of cavitation and acoustic stream. Cavitation is minimized and limited to the tip [54]. When energy is applied to the tip of the instrument, pressure drops, and small air bubbles are formed which create a high-energy shock wave during their explosion. The bursting of bubbles causes microshocks and rapid fluid movement, which can disrupt the biofilm [55]. UA of NaOCl considerably enhances the efficiency of purifying the root canal area [56] including the best lateral canal penetration [57], antibacterial properties [58], boosting the dissolution of necrotic tissues [59] as well as the organic part of smear layer [60]. UA of NaOCl in 3 cycles from 10 to 20 s for each root canal (with irrigant renewal after each cycle) is considered as a sufficient time to achieve cleansed canals. Ultrasound seem much less efficient in enhancing the activity of EDTA. Nevertheless, they might contribute to better removal of the smear layer as mentioned before [54]. When utilizing UA, the operator must insert the instrument point 1 mm prior to the working length [58]. Ultrasound activation is also beneficial for endodontic treatment conducted during multiple visits, giving the possibility to get rid of Ca(OH)\({}_{2}\) from the walls of root canals more efficiently in comparison to the manual syringe [61]. Using tips for UA increases the risk of instrument fracture within the root canal or escape of irrigant outside of the apical foramen [62]. The operators must be careful not to touch the canal walls with a tip, as this could result in a dampening effect or alter the canal wall's structure [56].
## 2 Materials and Methods
### PEF Treatment  
The PEF treatment was applied with a semiconductor-based positive Marx modulator [20]. For semi-pilot scale line (400 kg/h), model EPULSUS-PM1-10 (Energy Pulse Systems, Lisbon, Portugal), with a maximum pulse voltage of 10 kV, maximum pulse current of 200 A and 3 kW output average power, equipped with a DN25 colinear treatment chamber, with 2.5 cm between the electrodes was used. For the industrial scale line (4000 kg/h), model EPULSUS-PM2B-10 (EnergyPulse Systems, Lisbon, Portugal) with a maximum pulse voltage of 10 kV, maximum pulse current of 400 A and 6 kW out-put average power, equipped with a DN50 colinear treatment chamber, with 5 cm between the electrodes was used. On both scales, the applied electric field was 2 kV/cm in all trials with a 30 \(\upmu\)s pulse width at a frequency of 90 Hz. The measured pulse current was approximately  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**1 st Pilot Tests**} & \multicolumn{2}{c}{**2 nd Pilot Tests**} & \multicolumn{2}{c}{**3 rd Pilot Tests**} & \multicolumn{2}{c}{**Industrial Tests**} \\ \cline{2-9}  & **Control** & **PEF** & **Control** & **PEF** & **Control** & **PEF** & **Control** & **PEF** \\ \hline Olive fruits & & & & & & & & \\ Olive cultivar & & & & & & & & \\ Water (\%) & 56.18 & 56.45 & 61.49 & 61.13 & 61.15 & 65.55 & 62.61 & 63.10 \\ Oil (\% dry weight-RMN) & 43.61 & 43.29 & 44.4 & 42.51 & 45.51 & 46.34 & 44.79 & 45.55 \\ Oil (\% fresh weight-RMN) & 19.11 & 18.85 & 17.09 & 16.52 & 15.67 & 15.96 & 16.05 & 15.89 \\ Operation parameters & & & & & & & & \\ Malaxation time (min) & 90 & 45 & 30 & 30 & 30 & 30 & 30 & 30 \\ Decanter mass flow (kg h\({}^{-1}\)) & 384 & 384 & 310 & 310 & 318 & 384 & 2400 kg/h & 2400 \\ Decanter water flow (L h\({}^{-1}\)) & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ Temperature before PEF (\({}^{\circ}\)C) & 28 & 28 & 22 & 22 & 21.2 & 18.3 & 16 & 16.4 \\ Temperature after PEF (\({}^{\circ}\)C) & 30 & 30 & 24 & 24 & 21.8 & 21.9 & 17 & 18.1 \\ Oil flow vertical centrifuge (kg h\({}^{-1}\)) & 53.8 & 49.7 & 32.8 & 35.5 & 29.9 & 33.3 & 432.0 & 420.0 \\ Water flow in VC (L h\({}^{-1}\)) & 20 & 20 & 20 & 20 & 20 & 20 & 100 & 101 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Content of water and oil of olive fruits and operating parameters for the extraction trials conducted with fruits of cultivars ‘Manzanilla’ and ‘Hojiblanca’ extracted with a standard protocol (Control) and by applying pulsed electric field (PEF) technology.  
50 A for an applied specific energy of 5 kJ/kg to olive. In all the extraction trials the PEF device was installed between the mill and the malaxer. Control oils were obtained with the PEF device installed but disconnected.
## 3 Results and Discussions
### Machine Learning Classifications
#### 3.2.1 Standard Statistical Features  
Using standard statistical features, the highest classification performance was achieved using the logistic regression model with an accuracy of 67.4%. Figure 4 illustrates the accuracy, standard deviation, precision, recall, f1-score, specificity, and sensitivity of the reported classifiers.
## 4 Research Methodology  
This study aimed to evaluate and improve the practical applicability and robustness of AROS. Figure 2 illustrates the overall process divided into two distinct steps: the existing prototype is referred to as AROS_\(\alpha\), while the improved version resulting from this study is referred to as AROS_\(\beta\).  
First, AROS_\(\alpha\) was deployed on a bridge construction site by experienced safety and quality inspectors who used the functionalities introduced in Section 3. They were also asked to specifically evaluate the stability and locational accuracy of the model projections. Subsequently, a Focus Group Interview (FGI) was conducted with the inspectors to identify and collect potential technical issues with the system. The FGI enables people with similar backgrounds to discuss and exchange expert opinions based on their professional experiences, while a mediator helps to provide focus on individual issues [36].  
The group consisted of eleven experts in safety and quality management with an average of 13 years of experience in engineering and construction firms, as well as public agencies.  
Next, a causal analysis was performed to determine the reasons behind these issues, and approaches to resolve them were investigated and formalized. Finally, these improvements were updated in AROS_\(\beta\) and verified by deploying them on an identical site and quantitatively measuring their improvements.
# SIM-D: An Agent-Based Simulator for Modeling Contagion in Population
## 6 Evaluation
### SIS Infection Diffusion in Population  
To show the ability of SIM-D in modeling diseases, we ran a simulation for simulating the flu influenza over a population network of 100,000 agents. The disease transmissibility was set at 0.040. As is the case with flu influenza, we set the stay in infectious state at 7 simulation days. The incubation duration was set at 1 simulation day, which is a typical latent period for an infected person (with flu flu influenza) before he starts infecting others. At the start of the simulation, 2000 people were randomly marked as infected.  
Figure 7 shows that as the simulation progresses through steps, more people get infected, while the number of susceptible people sharply reduces. The epidemic peaks around the 40 th iteration. It is interesting to note that at any time there were no more than than 10% population infected at one time. However, over the course of the simulation, 80% of people got infected. The reason for a high number  
Figure 6: Modeling of SIR using SIM-D. The number of people in different states varies as the simulation progresses through steps.  
of total infections is the higher transmissibility of disease. Moreover, no intervention measures were applied, which leaves a major portion of population available for getting infected.  
Technically, SIS allows re-infections, however, we do not get reinfections in this study. The reason for this is because in flu influenza, there is a small chance that a person might get infected in 90 days. To mimic this behavior, a recovery period of 90 days is applied. This means that the re-infection could happen after the person becomes susceptible again.