esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparative Study of Different Approaches for Islanding Detection of Distributed Generation Systems
## 4 Result and Discussion
### Result of Islanding Detection by the Auto-Grounding Method  
Auto-ground, anti-islanding protection is based on applying a three-phase, short-circuit to the islanded distribution system just prior to reclosing or re-energization. It is seen as a compromise in terms of cost and performance between the transfer-trip and local passive measurements. The auto ground system is installed just down-line of the utility protection device (substation breaker or inline recloser). In this configuration, following opening of the utility breaker, the auto-ground opens the substation side device, denoted SS and closes the AS effectively applying a three phase to ground fault. All DGs that have not already disconnected based on their anti-islanding protection will be forced to disconnect based on online protection.  
For the detection of islanding events, four passive methods are used; over/under frequency, over/under voltage, rate of change of power and rate of change of frequency. The use of passive methods along with the principle of auto-grounding has been used for the detection of the islanding conditions. Based on the limits defined, the state of the system was monitored for 42 distinct cases.  
Among the islanding (1) and non-islanding (0) states with the 42 sequences of events, half were in islanding state, while the other half of the events were not in islanding state as shown in Table 2. The developed auto-grounding method was implemented to detect these conditions. Furthermore, 20 of the total 21 islanded conditions (95.2%) and 19 of the total 21 non-islanded conditions (90.4%) were successfully detected, and listed in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**No.**} & **Frequency** & **Voltage** & **df/dt** & **dP/dt** \\ \cline{2-5}  & **(Hz)** & **(pu)** & **(Hz/sec)** & **(MW/sec)** & \\ \hline
1 & 49.977 & 0.911 & 0.748 & 44.039 & 1 \\
2 & 50.279 & 0.989 & 7.309 & 508.538 & 1 \\
3 & 49.986 & 0.935 & 0 & 1.981 & 0 \\
4 & 49.979 & 1.05 &  
For instance, the event number 3 (with frequency 49.986 Hz, voltage of 0.935 pu, rate of change of frequency of 0 Hz/sec and rate of change of power of 1.981 MW/sec) was detected to be in a non-islanding condition, while event number 7 (with frequency 50.043 Hz, voltage of 0.95 pu, rate of change of frequency of 2.364 Hz/sec and rate of change of power of 43.767 MW/sec) was detected to be in an islanding condition.  
Figure 5 shows the current and voltage waveforms in the event of islanding detection by the auto grounding method. In the event of islanding in 2.5 s, it takes the auto-grounding method 1.705 s for islanding detection. With the auto-grounding scheme grounding the lines, the current increases above 500 A at around 4.194 s and thus the line protection scheme disconnects the circuit breaker, thereby creating an anti-islanding scheme.
## 4 Discussion
Additionally, the elicitation with jasmonic acid increased the level of some of these EO compounds; specifically, the JA3 elicitation increased the levels of eugenol (from 17.59 to 24.88%) and limonene (from 0.64 to 0.88%). The elicitation with jasmonic acid also increased the level of \(\upalpha\)-pinene (in the JA1 sample) and \(\upbeta\)-pinene (in the JA1 and JA2 sample) in the basil essential oil [11]. Based on the previously published information discussed above, it may be supposed that the changes in the composition of basil EOs resulting from the JA elicitation may have been the cause of the increased antibacterial activity of the tested oils observed in the present study. The studied bioactivities of essential oils probably depend on the composition of the oil (concentration of individual components of the oil). As indicated in our previous study, the induction of individual components of essential oil was not dependent on the elicitor dose [22]. The different responses of basil plants (i.e., the lower content of some compounds in plants treated with the higher elicitor concentrations) are difficult to explain. Therefore, as assumed by many researchers, the effect of elicitation depends largely on such factors as the type and doses of elicitors and the species of the elicited plant [15; 43]. In many other studies, it was also observed that plants did not respond in a dose-response manner in terms of production of secondary metabolites. For example, elicitation with 10 \(\upmu\)M salicylic acid was more efficient for the induction of the phenolic content in purple coneflower tops (_E. purpurea_ L. Moench.) than 100 \(\upmu\)M salicylic acid [44]. Additionally, in a study conducted by [PERSON] et al. [16], 1 \(\upmu\)M jasmonic acid was more effective in induction of the biosynthesis of phenolic compounds in lettuce leaves than 100 \(\upmu\)M jasmonic acid. Similarly, in our previous research concerning elicitation with 1, 10, and 100 \(\upmu\)M jasmonic acid, only the 10 \(\upmu\)M elicitor dose caused a statistically significant increase in the sum of phenolic compounds [17].
## 3 Materials and Methods
### Waveform Prediction
#### 3.5.2 Regression Loss Layer  
The neural network to predict the wave shape is required to maximise on the accuracy of the shape. For this, a custom regression layer was created in Matlab, with the loss function altered to use the Pearson correlation coefficient \(r\) between the predicted wave shape \(A_{0}\) and the actual wave shape \(A_{t}\), as shown in Equation (5). Since \(|r|\leq 1\) and is proportional to correlative strength, taking \(1-|r|\) allows the loss to be minimised, thereby maximising the correlation.  
\[loss\ =1-|r| \tag{5}\]
## 11 **The amount of half-rate capacity usage**
### Fault Detection
#### 2.5.1 Defining the Network Quality Assessment Criteria  
Fault detection is the most straightforward step in self-healing systems and networks. The complete, correct call rate (CCC) was the method used in the first mobile operator for fault detection and was the basis of our work. Therefore, only one criterion was used to check the network status. As mentioned in the previous section, there are many indicators to measure the quality of the network in different sectors and parts, each of which examines the network from only one perspective and the statistical information provided by each index is based on one of the aspects of the network. The research team of Sofrehom Company provided a measure using indicators and criteria to evaluate the network condition. This criterion was obtained as the product of the following indicators:  
1. Congestion percentage in the signaling channel (SD-Cong)
2. Success in signaling channel adjustment (SDestab)
3. Certain percentage in the signaling channel (SDdrop)
4. Traffic channel congestion percentage (TCHcong)
5. Traffic channel assigning failure rate (TCHAssignFR)
6. Mean uplink and downlink signal quality (RxQual)
7. Disconnection in traffic channel (DCR)  
Equation (2): Calculating the qualitative measure of network performance:  
\[\begin{array}{c}\text{CCC }=(1-\text{SDcong})*\text{SDestab }*(1-\text{SDdrop})*(1-TCHcong)\\ *(1-TCHAssignFR)*RxQuality*(1-DCR)\end{array} \tag{2}\]  
As can be seen, the CCC index consisted of the product of the seven most important indices. Therefore, if only one index had an unfavorable situation, its value would affect the result and the value of the CCC index. Therefore, by examining the importance of only one index, the status of the most important indicators of the network could be acquired. The fault detection process was performed by checking the value and behavior of this index during one month of available data. In this process, a histogram diagram of the index's monthly behavior was drawn in the investigated area, and the knees of the graph were evaluated as characteristic points in the behavior of the index. This method was used to discretize the performance of an index in a period into general categories, such as good, normal, bad, and unacceptable in [38]. Our priority was to work on a set of records that were in worse condition. This meant that network performance management and optimization departments had to first check the network parts in a worse situation. After troubleshooting these parts, other parts of the network could be inspected and evaluated. Therefore, we focused on the worst aspects and cells of the network. Other analyses and reviews were conducted on this part, and we reviewed different parts of the network.
# Microbiological Safety and Health Properties of Markleted Fermented Root Vegetables
## 3 Results and Discussion
### Total Antioxidant Capacity  
#### 3.2.1 DPPH Assay Results  
The results obtained by the DPPH assay were expressed as Trolox equivalent (TE) per 100 g of f.w. and % of activity. They were presented in Table 2. The highest value was 11.13 mmol TE/100 g f.w., which corresponded to 43.76% of activity, and was found for pickled beetroot. According to the literature, activity in scavenging the free radicals for fresh beetroot amounted to 63% [28]. Nevertheless, fermented beetroot was characterized by the greatest TAC of all the analyzed pickles. Radish was the root that was ranked second in terms of scavenging the free radicals (3.67-4.82 mmol TE/100 g f.w.). These results might be due to the high content of hydrophobic antioxidant compounds in radish, such as isothi cyanates [70]. However, apigenin from celeriac and carotenoids from carrots are hydrophobic antioxidants as well [71; 72]. Despite their high amount in the mentioned vegetables, the results obtained in the DPPH assay for these roots were much lower. The lowest TAC was found for fermented carrot, i.e., 1.47-3.21 mmol of Trolox/100 g f.w., and celeriac--1.89-4.41 mmol TE/100 g f.w. Moreover, a statistically significant difference was observed between carrot and beetroot, as well as between white radish and beetroot (\(p\) = 0.000). No significant differences were found among the other root vegetables.  
There is limited research on the antioxidant potential of fermented root vegetables. Furthermore, the DPPH assay is associated with considerable methodological discrepancies across studies [31]. Nevertheless, it has been demonstrated that the ability of fermented beetroot to scavenge free radicals increases during the first 7 days of fermentation, after which it begins to decline. Consequently, products stored for extended periods may exhibit lower total antioxidant capacity (TAC) compared to freshly fermented pickles [73]. Fresh counterparts of several roots cultivated in Poland were characterized by significantly lower levels of Trolox equivalents, as measured by the DPPH assay, i.e., 211 mmol TE/100 g f.w. in beetroot, 132 mmol TE/100 g f.w, in celeriac, and 42.5 mmol TE/100 g f.w. in carrot [74]. According to the cited study, the fermentation process may enhance the ability of processed products to scavenge free radicals.
# A Novel Real-Time PV Error Handling Exploiting Evolutionary-Based Optimization
## 4 Results  
In this section, the results of the proposed method are presented. Initially, the performance and accuracy of the proposed GA applied to model single-diode PV systems under various illuminance conditions are addressed. The PV parameters used in the experiments are outlined, and the sensors and meters used are presented while detailing the overall procedure that involves measuring real PV values, expected MPP values through the GA, and the subsequent comparison. The GA's accuracy is investigated by altering parameters affecting PV output, such as temperature and irradiance. Additionally, the GA's key hyperparameters, including population size, number of generations, crossover and mutation probabilities, are thoroughly tested to optimize its performance. The accuracy and efficiency of the GA are further compared with other optimization algorithms, showcasing the GA's efficacy in solving complex PV optimization problems. The results highlight the GA's ability to provide accurate predictions and its suitability for real-time PV monitoring and optimization, making it a valuable tool for practical applications.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameters** & **Description** & **Unit** \\ \hline \(I_{gen}\) & Output current of installed PVs & A \\  & as calculated from genetic algorithm & \\ \(V_{gen}\) & Output voltage of installed PVs & V \\  & as calculated from genetic algorithm & \\ \(P_{gen}\) & Output power of installed PVs & W \\ \(I_{act}\) & Output current of installed PVs & A \\  & as measured from meters & V \\ \(V_{act}\) & Output voltage of installed PVs & V \\  & as measured from meters & W \\ \(P_{act}\) & Output power of installed PVs & W \\  & as measured from meters & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters to compare actual values from predicted.
# An Efficient Distalization Technique Using Coil Springs and Mini Screws--A Finite Element Analysis
###### Abstract  
Background: More efficient molar distalization is demanded in orthodontics to shorten treatment times. In the present study, we propose a novel technique to improve force distribution to distalize molars more efficiently by using open-coil springs and an anchor screw. We conducted a finite element analysis to assess the efficiency of the proposed technique. Methods: A three-dimensional finite element model of an upper dental arch with brackets and an archive was constructed based on cone-beam computed tomography. We analyzed two distalization methods: a conventional grouped distalization technique (NoSp model), and our proposed technique using open coils (Sp model). Finite element analyses were performed to evaluate long-term tooth movement in both techniques. Results: The distalization force was more evenly distributed in the Sp model than in the NoSp model. Moreover, less concentration of compressive stress in the periodontal ligament (PDL) was observed in the Sp model. The force systems of the two models became more similar as the distalization progressed. However, the NoSp model still showed higher stress concentration at the end of the simulation. Conclusions: Inserting open-coil springs between distalized teeth improved the distribution of the force significantly. The conventional grouped distalization method might cause stress concentration at the first premolar, indicating risks of the hyalinization of the PDL and root resorption.  
molar distalization; finite element analysis; anchor screw; biomechanics Article  
An Efficient Distalization Technique Using Coil Springs and Mini Screws--A Finite Element Analysis  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], N. An Efficient Distalization Technique Using Coil Springs and Mini Screws--A Finite Element Analysis. _Appl. Sci._**2022**, 12, 10346. [[https://doi.org/10.3390/app122010346](https://doi.org/10.3390/app122010346)]([https://doi.org/10.3390/app122010346](https://doi.org/10.3390/app122010346))
# Integration of a Mobile Node into a Hybrid Wireless Sensor Network for Urban Environments +
## 3 Mobile Node  
As seen in Section 1, WSN are a solution for acquiring information close to the area of interest with persistence over time. However, a successful application requires attention to network coverage, connectivity or network overhead. Hybrid wireless sensor networks can provide a solution to some of these challenges. This section presents a mobile node architecture and implementation, and its integration with a static WSN to obtain an H-WSN.
## 2 Materials and Methods
### Satellite Data
#### 2.3.2 RapidEye Data  
The RapidEye satellite constellation consists of five identical small satellites and is capable of collecting a large volume of data covering over 6 million square kilometres per day [89]. The five satellites contain identical sensors and are equally calibrated and travel on the same sun-synchronous orbit at an altitude of 630 km. RapidEye offers spectral data in five bands from 440 to 850 nm with a red edge band at the central wavelength at 710 nm. We used the RapidEye image acquired for Schiermonnikoog on 18 July 2015, concurrent with the time of field campaign. The image was systematically geo-corrected and ortho-rectified at a spatial resolution of 5 m [90]. To obtain the TOC reflectance data, ATCOR-2 was used for atmospheric and topographic correction [91]. The corrected image was used to retrieve the average reflectance of each sampled plot in 2015. As the pixel size was 5 m, a pixel window of 3 by 3 (i.e., 15 \(\times\) 15 m) centred around the central position of a plot was used for collecting grass reflectance for each sample plot (20 \(\times\) 20 m). Figure 3 demonstrates the extracted reflectance of the sample plots in 2015 and 2016 from RapidEye and Sentinel 2 images, respectively.
# Analysis of Nanofluids Behavior in Concentrated Solar Power Collectors with Organic Rankine Cycle
## 2 Mathematical Model  
The mathematical model was established based on the mass and energy equations written to describe the behavior the nanofluids driving an Organic Rankine Cycle, ORC, with a refrigerant mixture as shown in Figure 1. This figure depicts the proposed CPS system. The CPS thermal loop is composed of a CPS solar collector, thermal storage tank, and pump, as well as the waste heat boiler of the ORC. The solar radiation is absorbed by the CSP solar collector and converted into thermal energy that heats up the nanofluids and thermal oil circulating in the thermal oil/storage tank loop. The thermal oil is the base fluid heat transport fluid with nanofluids is used to drive the ORC circuit.  
The thermal storage tank is used to store thermal energy, and during the intermittent solar radiation periods, it supplies heat to the ORC waste heat boiler. In the ORC circuit, the thermal heat absorbed evaporates the refrigerant mixture that drives the turbine. In the turbine, the thermal energy is converted into kinetic energy and produces power at the turbine shaft and the generator. The low-pressure vapor is condensed in the condenser into liquid and pumped back to the waste heat boiler. The refrigerant mixture used in the ORC loop is an environmentally sound quaternary mixture composed of R134a, R245 fa, R125, R236 fa [23]. Thermodynamic and thermophysical properties were obtained at REFPROP [27]. Figure 2 depicts the thermodynamic properties and the pressure-enthalpy diagram of the mixture R134a, R245 fa, R125, R236 fa, where it is quite clear that one of the most  
Figure 1: Flow diagram of the proposed system.  
important characteristics is the variable saturation temperature and glide temperature. This feature is pivotal in enhancing the Organic Rankine Cycle efficiency.  
In the following sections, the different equations of mass and energy are presented. It is assumed in the model that the nanofluid is homogeneous, isotropic, incompressible, and Newtonian; that inlet velocity and inlet temperature are constant; and that the thermophysical properties of the nanofluids are constant. In the following, the conservation mass and energy equations and heat transfer equations are written and presented for each element of the nanofluids.
## 2 SLAM Based on Multi-Sensor Fusion
### Description of Algorithm  
On the basis of 2D LiDAR and depth vision sensors, this paper proposes a SLAM framework for autonomous vehicle localization and mapping based on multi-sensor information fusion to make up for the problem that low obstacles in the environment cannot be perceived. The algorithm realizes the information fusion of 2D LiDAR and depth vision sensor, improves the vehicle's environmental perception ability, increases the vehicle's ability to detect obstacles in a complex environment, avoids the disadvantage of low detection accuracy due to the performance of single sensor, and improves the SLAM accuracy and stability of a mobile robot. The overall framework of the algorithm is shown in Figure 1.  
The overall process of the algorithm is mainly divided into the following four parts:  
* Sensor calibration: The camera, LiDAR, and IMU are calibrated in time and space, and therefore the data information output by the sensor is on the same plane and time.
* Data preprocessing: Data processing of laser point cloud and camera information. The IMU is pre-integrated to obtain the relative pose transformation matrix of the vehicle, which provides the transformation basis for the LiDAR point cloud frame matching.
* EKF data fusion: The extended Kalman filter is used to fuse the laser detection data and the visual detection data to generate a local map.
* Map construction and loopback detection: The real-time pose of the robot is used as the center of the circle, R is the radius to delineate the detection circle, and the laser point cloud ICP algorithm is used to perform loopback detection on the robot trajectory.
# Impact of Die Radius in a Streamlined Die during Wire Drawing
## 4 Results
### Comparison of Stress and Damage  
Figure 8 compares the contour of the effective stress of the wire with the die radius. The stress was concentrated at the surface area with decreasing die radius. The wire using the die with a radius of 64 had a similar contour of effective stress to the wire using the conventional die. The die with a radius of 128 improved the stress distribution of the wire compared to the conventional die, which is also confirmed by the comparison of the maximum effective stress, as shown in Figure 9a.  
It is necessary to analyze the damage value of the workpiece during the plastic forming process to evaluate the formability. The normalized Cockcroft and Latham fracture criterion [39; 40] was applied in this study using the following equation:  
\[D=\int_{0}^{\varepsilon_{f}}\frac{\sigma^{*}}{\overline{\sigma}}d\overline{\sigma} \tag{7}\]  
where \(\sigma^{*}\) is the highest tensile stress and \(\overline{\sigma}\) is the effective stress. Figure 8 compares the damage values of the wire for different values of die radius. The center area had the highest damage value, and the surface area had the minimum value regardless of the die radius. The damage value decreased with increasing die radius, as shown in Figure 9b. For example, the wires manufactured by dies with radii of 4 and 16 had higher damage values  
Figure 8: Comparison of contour in effective stress and damage value of the wire with the die radius.  
Figure 9: Comparison of (**a**) the maximum effective stress and (**b**) damage value along the radial direction of the wire with the die radius.  
compared to the conventional die, and the wires manufactured by the dies with radii of 64 and 128 had lower damage values compared to the conventional die. In particular, the damage value of the wire using the die with a radius of 128 was significantly reduced compared to that of the conventional die, and the profile of the damage value by the die with a radius of 128 was uniform along the radial direction of the wire.