esa-sceva/satcom-chunk-collection huggingface dataset:
# Unity Power Factor Operation in Microgrid Applications Using Fuzzy Type 2 Nested Controllers
## 6 Conclusions  
The proposed Fuzzy type 2 controllers improved the power factor of single-phase MGs in both the simulations using MATLAB/Simulink and experiments using a 10-kHz-sampling dSPACE platform. Step changes in the DC link voltage and AC load were examined (increase and decrease). The simulations and experimental results showed that the suggested Fuzzy type 2 controller outperformed the other two systems in the dynamic response, steady-state error, and compliance with the power quality standards. In the case of the DC-link voltage step change, the proposed controller reduced the THD by 40.7% and improved the power factor by 4.56%. In the case of the AC-load step change, the proposed technique reduced the THD by 51.5 % and improved the power factor by about 4.73%. In both cases, the results comply with the requirements of the power quality standards. Unlike meta-heuristic optimization approaches, this paper predicted the FLC parameters using deterministic optimization, ensuring an optimal solution. Additionally, the FLC technique was used to address power quality concerns using DC microgrids, which is uncommon in the literature. The proposed technique can be easily applied to various systems including various structures of MGs and other power system applications.  
Investigation of the influence of nested loop controllers on each individual loop could be handled in a separate paper, which could be an extension of the current research.  
The sensitivity of the proposed controller to the parameter variations was not studied, which could be the direction of further research. However, the controller design should  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **Simulation** & **Experimental** \\ \hline Displacement factor & 0.991 & 0.972 \\ \hline Total Harmonic Distortion & 2.09 & 3.11 \\ \hline Power Factor & 0.9905 & 0.972 \\ \hline IEEE 1547 Standards & AGREE & AGREE \\ \hline \hline \end{tabular}
\end{table}
Table 6: Source current performance parameters in the simulation compared to the experimental results.  
involve the disturbance rejection capability by considering other methods such as invariant-set and \(H_{\infty}\).  
Conceptualization, H.A. and E.H.E.B.; Methodology, E.H.E.B., A.M.I. and H.A.; Software, A.M.I., H.A. and E.H.E.B.; Validation, M.D.S., E.H.E.B., A.M.I. and H.A.; Formal analysis, M.D.S., E.H.E.B., A.M.I. and H.A.; Investigation, M.D.S., E.H.E.B., A.M.I. and H.A.; Resources, H.A. and E.H.E.B.; Data curation, M.D.S., E.H.E.B., A.M.I. and H.A.; Writing--original draft preparation, M.D.S., E.H.E.B., A.M.I. and H.A.; Writing--review and editing, M.D.S., E.H.E.B., A.M.I. and H.A.; Visualization, M.D.S., E.H.E.B., A.M.I. and H.A.; Supervision, E.H.E.B. and A.M.I. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Markov Chain Monte Carlo Based Energy Use Behaviors Prediction of Office Occupants
## 3 Proposed Method
### Model Training  
The energy use action model refers to the probability that the electrical equipment enters the power-on and power-off state within a certain temperature; the energy use working hours model refers to the probability of the electrical equipment duration of energy use; and the energy use behavior model of air-conditioner refers to the probability that the air-conditioner enters into the cooling and heating state at a given temperature. Since time and temperature are continuous variables, which are difficult to determine for the entire posterior distribution, the MCMC was used to study the approximate distribution and obtain the fitting curve of the data distribution.  
The energy use action model and the energy use working hours model reflect the time rule of user's energy use behavior. The air-conditioner energy use behavior model reflects user's thermal comfort, which depends on user's comfortable temperature range. The process of model training was as follows, taking the energy use action model as an example to elaborate in detail. At first, we needed to observe the electrical equipment's state distribution based on the time series to determine the fitting function with the parameters that needed to be trained, set the number of samples, and give the initial value to the parameters for the sampling. Secondly, we assumed the a priori distribution of the parameters, and MH random sampling was carried out by searching the distribution space of the parameters. Based on Markov characteristics, new values were randomly assigned to the parameters according to the current state. Thirdly, we checked the new random values; if they satisfied the MH sampling theorem, then we accepted them as a new current state; if not, we rejected and returned them to the previous state. Lastly, we repeated the previous two steps for the specified number of iterations, until it reached the detailed condition of a Markov chain. This method could construct the energy use behavior model based on different time ranges (daily, weekly, annually, etc.), and the calibration frequency of the model needed to be adjusted accordingly. We took the construction of the day based energy use behavior model as an example to evaluate the performance of the proposed method in this paper. The flowchart of energy use behavior prediction is shown in Figure 3.  
Figure 3: Flowchart of energy use behavior prediction.  
The algorithm returned all the values generated by the parameters, and the mean of these values was used as the most likely final value for the parameters in the model. We used the Bernoulli function to calculate the probability of the equipment action time. The MCMC returned an approximation of the distribution instead of the accurate results.
# Peri-Inplant Tissue Stability: A Series of Five Case Reports on an Innovative Implant Design
###### Abstract  
Background/Aim: The stability of peri-implant tissues is crucial for the long-term success of dental implant treatments. A new cervical implant design has been developed to address the challenges associated with peri-implant tissue stability, featuring a concave cervical portion to increase tissue volume in this area. The present study aimed to clinically evaluate the effectiveness of the new cervical implant design in maintaining peri-implant tissue stability. Materials and Methods: Five clinical cases involving completely edentulous patients were selected, in which 25 implants were installed. The marginal bone level around each implant was assessed at three different time points--T0: immediately after the prosthesis installation, T1: 6 months post installation, and T2: at the last control visit, up to 38 months later. Measurements were taken to analyze changes in marginal bone levels (MBLs) and the keratinized mucosa (KM) over time. Furthermore, the keratinized mucosa (KM) around the implants was evaluated. Results: The mean and standard deviation values of the marginal bone levels at each time point were as follows--T0: 0.59 \(\pm\) 0.55 mm; T1: 1.41 \(\pm\) 0.59 mm; T2: 1.76 \(\pm\) 0.69 mm. Statistical analysis showed significant differences across the time points (ANOVA \(p<\) 0.0001). The overall mean KM values were 3.85 mm for T1 and T2, showing the stability of the peri-implant soft tissues at \(\geq\)1-year controls. Conclusion: Within the limitations of the present study, the results showed that the Collo implants presented measured MBL values increasing within the time range analyzed in each case but within the normal values cited in the literature for these types of rehabilitation treatments. However, the measured KM values presented, in all cases, an average above the values referenced in the literature as a minimum for maintaining the health of the peri-implant tissues.  
Keywords:dental implants; clinical cases; new implant design; bone level; keratinized mucosa +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Sequential Batch Photobioreactor Experimentation with Duckweed and microalgae
#### 3.3.4 Effect on Biomass Production and the Proximate Chemical Composition of Microalgae
\({}^{\rm II}\)There parts of DWW and one part of BCI11 medium, \({}^{\rm III}\)whey water to BCI11 media in the ratios of \(13.7\) (or) \({}^{\rm I}\)OC total organic carbon \({}^{\rm II}\)The depletion of nitrates observed at all TRW concentrations and the depletion of phosphates and sulfates only at TRW10% for _Chlorella_ (Figure 7) indicate efficient nutrient uptake and utilization by both microalgae species. This efficient nutrient assimilation likely contributes to the observed growth parameters, with maximum specific growth rates, biomass productivities, and biomass yields being highest at TRW-20% for both microalgae. The superior performance of _Chlorella_ in terms of growth parameters suggests its greater adaptability to this type of nutrient-rich wastewater compared to _Scenedesmus_. This contrasts with a previous study reported with the same species, where _Scenedesmus_ consistently outperformed _Chlorella_ when explosives production effluents were bioremediate [13]. This is likely because _Chlorella_ is more resistant than _Scenedesmus_ to wastewater with high organic carbon loads and assimilates COD more efficiently [68]. Overall, the observed changes in the biochemical composition of microalgae reflect their metabolic responses to varying nutrient availability in the TRW-based culture medium. These responses are intricately linked to growth parameters, with nutritional availability influencing metabolic processes and biomass production. Understanding this interplay between biochemical composition of microalgal biomass, nutrient availability and types in TRW, and growth parameters is crucial for optimizing sequential batch photobioreactor strategies with duckweed and microalgae to adjust the biomass produced to the standards required for different applications such as biofuel production, bioremediation, nutraceuticalals, aquaculture, and animal feed. In fact, _Chlorella_ and _Scenedesmus_ grown in TRW yield proximate compositions that are within the ranges previously reported for these genera with other culture media [69]. This information could be valuable for scaling up microalgae-based RW bioremediation processes and using the resulting biomass for various applications.
# Error Sources of Interferometric Synthetic Aperture Radar Satellites
## 4 Random Errors
### Volume Decorrelation (\(\delta_{volume}\))  
The baseline decorrelation (previous subsection) was derived with an assumption that the returned signal comprised only surface scattering (Figure 9 Left) [30; 127; 148]. However, different media are more conductive to volume scattering (e.g., vegetation, sand, and icy terrain) (Figure 9 Right) [30; 127; 149; 150].  
The ability of the radar signal to penetrate the ground surface is determined by the incident wavelength as well as the dielectric constant of the scattering medium. The vertical vector (\(\Delta z\)) of the penetration within a resolution cell causes dispersion of the scatterers [69], so the vertical vector is the decisive factor for volume decorrelation. As the backscatter phase from a given pixel is extended over a larger projected area along the vertical vector, a thicker scattering layer leads to more severe loss of coherence until it reaches a critical thickness (\(\Delta z_{c}\)). This is defined in Equation (25), where volume correlation becomes 0 (Equation (26)) [36; 127].  
\[\Delta z_{c} =\frac{\lambda R\sin\theta}{2B_{\perp}} \tag{25}\] \[\gamma_{Volume} =sinc\bigg{(}\frac{\Delta z}{\Delta z_{c}}\bigg{)} \tag{26}\]Figure 8: Frequency shift as a function of the local terrain slope in the case of NISAR S-band with four different perpendicular baselines (\(-500\) m, \(-1000\) m, \(-5000\) m, and \(-10,000\) m). The colored lines and regions in these subplots follow the same meanings as in Figure 7.  
Figure 9: Illustration of volume decorrelation. A stands for antenna. The subscript 1 and 2 symbolize the first and second acquisition. \(\Delta\)z is the vertical vector of the scattering layer. B is baseline between A\({}_{1}\) and A\({}_{2}\). B\({}_{\perp}\) is perpendicular baseline. (**Left**) Interferometry viewing geometry with the assumption that the returned signal is only comprised of surface scattering. (**Right**) Interferometry viewing geometry which considers the influence of a scattering layer.
## 7 Integrated Localization and Communication
### Localization for RISeUAVs  
The localization-oriented study for UAV-enabled, RIS-assisted communication can be considered from two perspectives:  
(1) Positioning-aware beamforming of the aerial RIS in the RISeUAV.  
(2) Accurate localization of RISeUAV and ground vehicles.
# Discretly Exploiting Inter-Session Information for Session-Based Recommendation
## 5 Experiments
### Overall Performance(RQ1)
(3) Among models that solely rely on intra-session information, Disen-GNN achieves the best performance, even beating some models that consider both inter-session and intra-session information. The incorporation of a DRL technique enables Disen-GNN to explore the limited intra-session dependency semantics at the factor level, thereby leading to superior recommendation outcomes. (4) DEISI outperforms all the baseline models across all datasets, achieving a significant improvement in recommendation accuracy of approximately 8.5% in Nowplaying, 8% in Yoochoose 1/64, and 3% in Diginetica, especially demonstrating remarkable performance in the Nowplaying dataset. Our analysis suggests that there are three factors contributing to the superior performance of DEISI. First, DEISI utilizes Graph Neural Networks (GNNs) to establish item and session relationships, facilitating better embedding learning. Second, both DEISI and Disen-GNN construct dependencies at the factor level, with DEISI demonstrating better performance due to its incorporation of additional inter-session information. Finally, DEISI employs a discreet strategy to effectively learn inter-session information, while avoiding noise and redundancy that could interfere with the learning process. As a result, DEISI outperforms the baseline models.
## 3 Development of the Hole Diameter Measuring Machine
### Four-Degree-of-Freedom Attitude-Adjusting Worktable  
Precise and multi-degree-of-freedom adjustment of hole attitude also need to be thoroughly considered to realize ultraprecision measurement of deep and small hole diameters. The hole attitude must be precisely adjusted to make its axis parallel to the Z axis of the HDMM before diameter measurement. This was realized through a 4D attitude-adjusting worktable on which the part to be measured was mounted.  
The inclination angles of the hole axis in the \(\theta\)x and \(\theta\)y directions with respect to the Z axis of the HDMM needed to be acquired to provide reference values for adjustment. The principle of determining an inclination angle from the 3D probing capability of the SSEP probe is shown in Figure 10. By successively probing the sidewalls of an upper section and a lower section, \(l_{1}\), \(l_{2}\), and \(h\) could be obtained, and then the inclination angle \(\alpha\) could be calculated according to Equation (2).  
After acquiring inclination angles \(\alpha\) and \(\alpha^{\prime}\) in two directions, the 4D worktable was rotated by \(-\alpha\) and \(-\alpha^{\prime}\) in respective directions. The process of acquiring inclination angles and adjusting the worktable could be repeated until the inclination angles were smaller than a specified threshold. The experimental results show that two to three adjustments of the worktable are typically sufficient to reduce the inclination angle of the hole axis to a negligible value, thereby satisfying the measurement requirement.  
It should be noted that the hole is assumed to be cylindrical here because the holes to be measured in the aviation, aerospace, and automotive industries are mostly cylindrical. Holes with a non-cylindrical shape are rarely seen. Also, because the precision of precisely manufactured holes in the aviation, aerospace, and automotive industries is usually in the micron order, misalignment resulting from differences at two different heights and the corresponding introduced measurement error is very small and can be ignored. Even for a truncated cone-shaped hole, the nominal value of its flank angle relative to its axis can be used to realize precision alignment.  
\[\tan\alpha=\frac{|l_{1}-l_{2}|}{h} \tag{2}\]
# BEAC-Net: Boundary-Enhanced Adaptive Context Network for Optic Disk and Optic Cup Segmentation
## 5 Experiments
### Implementation Details  
The BEAC-Net is implemented based on Python 3.6 and Pytorch 1.7.0 using Ubuntu 18.04 Linux and NVIDIA RTX3060 GPU for the experiments. For OD and OC partitioning, two public datasets were used as the training set to fit and optimize the model parameters. Subsequently, tests were performed on the internal dataset to evaluate the performance of the network. During training, the epochs are 300, and the popular SGD optimizer with a momentum value of 0.9 and weight decay value of 1 \(\times\) 10\({}^{-4}\) is used to optimize our model for backpropagation, along with a learning rate update strategy and an early stop mechanism. The learning rate decreases gradually from 0.001, and when the loss does not decrease after 10 training rounds, the network learning rate is reduced to half. During training, the batch size is set to 4, and the output segmented image size is 640 \(\times\) 640 \(\times\) 3.
## 4 Results and Discussion
### Second Application: Control of Tilt, Sidelobes and Nulls
#### 4.3.2 Analysis of the Simulations
In addition, there have been reports of difficulty in achieving nulls 40 dB below the main lobe [53]. On the other hand, considering 10 simulations for each operating condition tested, GA-MMC allowed values over 50 dB (peaking 77.0 dB), proving the repeatability and stability of results.
## 2 Materials and Methods
### Review and Evaluation of Current Test Scenarios to Develop a Comprehensive Test Run Database
\begin{table}
\begin{tabular}{c c c c c} \hline
**No.** & **Type 1** & **Document ID 2** & **Related Organization** & **Scope 3** \\ \hline
1 & P & EuroNCAP AEB C2C Test Protocol v4.3.1 & EuroNCAP & AEB \\ \hline
2 & P & EuroNCAP AEB LSS Test Protocol v4.3 & EuroNCAP & LSS \\ \hline
3 & P & EuroNCAP AEB SAS Test Protocol v2.0 & EuroNCAP & SAS \\ \hline
4 & P & EuroNCAP AEB LSS VRU Test Protocol v4.5.1 & EuroNCAP & AEB/LSS \\ \hline
5 & P & EuroNCAP AD Test and Assessment Protocol v2.2 & EuroNCAP & ACC AEB/LSS/SAS \\ \hline
6 & P & Technical Bulletin 037 ESS Assessment v1.0 & EuroNCAP & ESS \\ \hline
7 & R & UN Regulation No. 48 & UNECE & RECAS \\ \hline
8 & R & UN Regulation No. 79 & UNECE & CSF/ACSF/ESF/RMF \\ \hline
9 & R & UN Regulation No. 130 & UNECE & LDWS \\ \hline
10 & R & UN Regulation No. 151 & UNECE & BSIS \\ \hline
11 & R & UN Regulation No. 152 & UNECE & AEB \\ \hline
12 & R & UN Regulation No. 157 & UNECE & ALKS \\ \hline
13 & R & UN Regulation No. 159 & UNECE & MOIS \\ \hline
14 & R & UN Regulation No. 171 & UNECE & DCAS \\ \hline
15 & R & Regulation (EU) 2021/646 & European Commission & LDWS/CDCF \\ \hline
16 & R & Regulation (EU) 2021/1958 & European Commission & ISA \\ \hline
17 & R & Regulation (EU) 2023/2590 & European Commission & ADDW \\ \hline
18 & S & ISO 22737 & ISO & LSAD \\ \hline
19 & S & ISO 23374 & ISO & AVPS \\ \hline \end{tabular}  
* Types: R—regulation; S—standard; P—test or assessment protocol. 2 During the review, the latest available versions were considered; date of last update: 14 April 2025. 3 Meanings can be found in Abbreviations.  
\end{table} 
Table 1: List of reviewed and processed regulations, standards, and protocols utilized for test run database development.
# Communication System Based on Magnetic Coils for Underwater Vehicles
## 1 Introduction
### Acoustic Communication  
Acoustic waves are mechanical vibrations that propagate in a medium. Acoustic waves suffer less attenuation relative to the communication systems described above (\(\sim\)1 dB/km) [13]. However, this type of communication has other limitations, such as disturbance from a higher ambient noise level, unwanted reflections that take advantage of the same conditions for easy propagation, and lower data transmission rates.  
An application of this underwater communication technology is a network that coordinates autonomous underwater vehicles for mine inspection [14]. Additionally, acoustic communication has been used to exchange data between an unmanned surface vehicle and an autonomous underwater vehicle [15]. Another important application is the use of acoustic communication signals in an autonomous underwater vehicle to characterize the seafloor [16].  
Table 1 shows a summary of the characteristics of the technologies used in submarine communication [17].  
It has been observed that when implementing radio frequency communications, high data rates are achieved but are limited to a strong attenuation by the conductivity of seawater. On the other hand, optical communications provide much higher data rates. However, they are subject to seawater turbidity attenuation. Moreover, acoustic waves provide long transmission distances but have low data transmission rates and are subject to reflections due to multipath propagation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Acoustics** & **Radio Frequency** & **Optics** \\ \hline Speed of sound & \(\sim\)1500 m/s & \(\sim\)300,000 km/s & \(\sim\)300,000 km/s \\ Data speed & \(<\)100 Kbps & \(<\)10 Mbps & \(<\)1 Gbps \\ Power losses & \(>\)0.1 dB/m/Hz & \(\sim\)28 dB/km/100 MHz & turbidity \\ Bandwidth & kHz & MHz & \(\sim\)10–150 Hz \\ Frequency band & kHz & MHz & \(\sim\)10\({}^{14}\)–10\({}^{15}\) Hz \\ Limitations & Bandwidth & power & ambient \\ Effective range & km & \(\sim\)1–100 m & \(\sim\)1–100 m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of underwater communication systems.  
Due to the limitations of the current technology used in submarine communication, the design of a communication system based on rectangular magnetic coils is proposed. This system works through magnetic induction (MI), a technology that works just as efficiently underwater as it does on land.