esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Advanced MPPT Techniques
### Smart MPPT Techniques
#### 5.1.1 Fuzzy Logic Controller Algorithm  
The Fuzzy Logic Controller (FLC) MPPT algorithm uses a rule-based system to optimize the power output of PV systems by handling imprecise and uncertain data. It operates on the principles of fuzzy logic, where the control actions are determined by a set of \"if-then\" rules that model the relationships between input variables, such as voltage and current, and the desired output [56]. As shown in Figure 9, the FL technique has three stages. The first stage, fuzzification, converts numerical data into linguistic values using membership functions. In the second stage, the rule table processes these entries to make a decision. The final stage, defuzzification, converts linguistic data back into clear numerical values. The FL algorithm evaluates the difference between the current power and the power from the previous time step, as well as the rate of change in power, using fuzzy rules. The key advantage of the FLC algorithm is its flexibility and robustness in managing varying conditions and uncertainties, such as fluctuating irradiance or temperature changes, without needing a precise mathematical model of the system. This adaptability allows it to effectively handle complex scenarios and partial shading. The primary drawback is the complexity of the design and tuning of the fuzzy rules, which may necessitate a high level of expertise [57; 58]. The functions that indicate error (\(E\)) and change in error (\(\Delta E\)), with their corresponding equations shown below, are commonly  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Tracking** & **Sensed** & **Circurity** & **Tracking Speed** & **Implementation Complexity** & **True MPPT** & **Accuracy** & **Cost** \\ \hline P&O & V, I & A/D & Slow & Low & Yes & Medium & Low \\ IC & V, I & D & Medium & Medium & Yes & Medium & Medium \\ CV & V & A & Slow & Low & No & Low & Low \\ LT & G, T or I, T & D & Medium & Low & Varies & High & Low \\ HC & V, I & D & Slow & Low & Yes & Medium & Low \\ \hline \end{tabular}
\end{table}
Table 2: Comparison of traditional MPPT algorithms [34; 53; 55].  
used as inputs to the FL algorithm. In the given equations, \(P\) and \(V\) are module output power and module voltage, (\(n\)) represents the current value, while (\(n-1\)) indicates the value from the previous sampling period. Based on these equations, the control signal is generated to maximize power output.  
\[E(n)=\frac{P(n)-P(n-1)}{V(n)-V(n-1)} \tag{3}\]  
\[\Delta E=E(n)-E(n-1) \tag{4}\]
###### Abstract  
This paper addresses the classification of images depicting the eruptive activity of Mount Etna that were captured by a network of ground-based thermal cameras. This study aimed to evaluate the performance of Vision Transformers (ViTs), such as the Swin Transformer, compared with Convolutional Neural Networks (CNNs), including AlexNet and ShuffleNet. A dataset of 3000 images, evenly distributed across six classes, was utilized for training and testing. The results indicate that for this specific application, the performance advantage of Vision Transformers over CNNs was marginal, likely due to the nature of the classification task. While the Transformer-based models, like the Swin Transformer, demonstrated a slightly improved accuracy for certain complex classes, the CNN-based models, such as AlexNet and ShuffleNet, exhibited superior computational efficiency, particularly in terms of the classification speed. These findings highlight the suitability of CNNs for real-time volcanic activity monitoring. Additionally, this paper provides a comprehensive review of the various CNN and Vision Transformer architectures, offering insights into their strengths and limitations in the context of volcanic activity classification.  
Keywords:Vision Transformer; Swin Transformer; AlexNet; image classification; Etna activity; deep learning; real-time monitoring; thermal imaging; Convolutional Neural Networks (CNNs) +
Footnote †: journal: Applied Sciences
# 3D Volumetric Analysis and Anatomical Considerations for
## 2 Materials and Methods  
This retrospective study included 83 sinuses in 63 patients, who visited the Department of Oral and Maxillofacial Surgery for SBG and implant treatment between January 2015 and July 2020. The height of residual bone was less than 5 mm. The average age of the patients was 57.37 y and ranged from 28 to 92 y.  
For the preoperative evaluation, cone beam computed tomography (CBCT) was taken (Hitachi, Marunouchi, Japan) and three-dimensional (3D) reconstruction models were created by reformatting the CBCT scan images on Mimics (18.0.0.025) software (Materialise, Leuven, Belgium).  
For evaluating the position of PSAA, we measured the distance between the lower border of the artery and the lowest point of inferior border of maxillary sinus on coronal plane (Figure 1).  
Figure 1: The height of posterior superior alveolar artery (PSAA). Measured from the lower point of the maxillary sinus to PSAA.  
We also measured the thickness of lateral wall of sinus at right immediately below the external branch of PSAA (Figure 2).  
Sinus cavity was segmented for making the 3D model by setting of threshold value with Mimics software and then it was divided into 3 mm, 5 mm, 7 mm, and 10 mm heights based on the lowest point of the sinus floor and then the 3D segmented volumes were measured according to the height from the sinus floor (3 mm, 5 mm, 7 mm, 10 mm) and the total maxillary sinus volume (Figure 3).  
In order to measure the height of the residual bone, the vertical distance between the tangents of the lower point of the maxillary sinus and the crest of the residual bone was evaluated. A septum of maxillary sinus was identified in CBCT images (Figure 4).  
Statistical analysis was calculated using SPSS 20.0 (IBM, New York, NY, USA). The normal distribution was verified through the Kolmogorov-Smirnov test, and the statistical method according to each result was used. Statistical significance was verified through the Mann-Whitney U test, independent t-test and chi-square test. A \(p\) value of 0.05 was set for statistical significance.  
This study protocol was approved by the institutional ethics committee. Written informed consent was obtained from all of the patients. (CUDHIRB 1902007)  
Figure 3: 3D modeling of the maxillary sinus. Measuring the volume of the sinus by segmenting it into 3 mm, 5 mm, 7 mm, and 10 mm from the lower point of the sinus.  
Figure 2: Lateral wall thickness was measured at right immediately below the PSAA.
###### Abstract  
In modern power systems, fluctuations in load present ongoing challenges, making Load frequency control (LFC) an essential part of maintaining system stability and efficiency. This paper explores a method that combines traditional PID control with the Particle Swarm Optimization (PSO) algorithm to improve frequency regulation in interconnected hydropower systems. By using PSO, the method fine-tunes the PID controller parameters, enhancing frequency regulation, accelerating stabilization, and ensuring steady power flow across interconnecting lines. The simulation results show that this optimized method outperforms the conventional techniques, offering improved dynamic responses and a more robust performance, even in challenging and variable conditions. This makes it a promising solution for modern power systems, particularly in managing load fluctuations and maintaining frequency stability in interconnected hydropower systems.  
Keywords:load frequency control; particle swarm optimization; PID controller; hydropower system stability; interconnected power systems +
Footnote †: journal: Applied sciences
## 4 Discussion
### The Need for Fishing Vessel Monitoring with Nighttime Remote Sensing
This suggests that when extracting and interpreting fishing vessel data from nighttime light imagery, special attention must be given to the meteorological conditions in specific fishing grounds. Adverse weather conditions may limit the accuracy of nighttime light remote sensing, preventing it from fully reflecting real-world vessel activity. It is also important to note that while nighttime light remote sensing offers an effective supplementary method for monitoring lighted fishing vessels, it is insufficient for identifying the vessels' identities. Nighttime light imagery can confirm the presence of vessels but cannot determine their nationality or whether they are engaging in illegal, unreported, and unregulated (IUU) fishing. To accurately identify these vessels and assess their compliance with regulations, AIS data and the NPFC's list of registered vessels permitted to operate must be integrated. Additionally, with the launch of the VIIRS satellite series (including NPP, JPSS1, and JPSS2), the frequency of observations further increased, allowing for even more detailed monitoring [37; 38]. Therefore, it is of critical importance to continue developing precise and efficient monitoring methods and processes for light fishing vessels based on nighttime remote sensing. This advancement will provide significant support for the accurate and comprehensive monitoring of light fisheries on the high seas, ensuring better management and sustainability of these vital resources.
# Distribution of Operating Costs Along the Value Chain of an Open-Pit Copper Mine
###### Abstract  
This study analyzes the distribution of operating costs along the value chain of an open-pit copper mine with a focus on key operational units or operations such as drilling, blasting, loading, hauling, stockpiling, blending, crushing, milling, and flotation. Using process costing analysis, key cost drivers were identified, and their individual contributions to total expenses were quantified. Results revealed that communication processes dominate the operational cost structure, with milling accounting for 6.18 USD/ton, representing 59.1% of total operating costs, and crushing costing 1.15 USD/ton, that is, 11% of total operating expenditure. The study also highlighted several opportunities for cost reduction and enhanced mining sustainability through strategies such as energy consumption optimization, the use of alternative energy sources, and optimized blast design. Finally, valuable insights aimed at promoting sustainable resource utilization, improved cost efficiency, and data-driven decision-making in mining operations are offered to mine planners and operators. This is eventually expected to lay the foundation for benchmarking work on the establishment of a baseline and standards for similar mining operations.  
open-pit copper mine; operating costs; mine planning; resource utilization; economic viability; mine value chain Article
# Fluorescence Hyperspectral Imaging for Early Diagnosis of Heat-Stressed Ginseng Plants
## 2 Materials and Methods
### Data Extraction and Analysis
#### 2.4.3 Ratio Image for the Differentiation and Visualization of Heat-Stressed Plants  
Initially, the obtained hyperspectral images were corrected as described in Section 2.3. Each imaged plant sample consisted of 128 bands ranging from 400 to 1000 nm. Then, the ANOVA-selected optimal wavelengths were divided (731.6 and 745.9 nm), as presented in Equation (1), for the band ratio image, generating a ratio image.  
\[\mathrm{I_{R}}=\mathrm{I_{B1}}\ (731\ \mathrm{nm})/\mathrm{I_{B2}}\ (745\ \mathrm{nm}) \tag{1}\]  
Whereas the equation presents the \(\mathrm{I_{R}}\) (image ratio), \(\mathrm{I_{B1}}\) (image band at 731 nm) divided by \(\mathrm{I_{B2}}\) (image band two 745 nm) is suggested by ANOVA.  
The thresholding value was determined by converting the ratio images to binary. The value was set based on the control and stressed plants' average median pixel intensity. This process was based on using the median threshold intensity responses to visualize variations between heat-stressed and control ginseng plants (see Figure 5). In this process, the value intensity was determined to be less than or equal to the threshold computed for the control plants. Otherwise, the plant was classified as a stressed plant. The total performance of the thresholding method was evaluated based on calculating the sensitivity, specificity, and total accuracy using Equation (2), Equation (3), and Equation (4), respectively.  
\[\mathrm{Sensitivity}=\left(\frac{\mathrm{TP}}{\mathrm{TP+FN}}\right)\times 100= \left(\frac{\mathrm{Correctly\ detected\ number\ of\ stressed\ plants}}{\mathrm{Total\ number\ of\ stressed\ plants}}\right)\times 100 \tag{2}\]  
\[\mathrm{Specificity}=\left(\frac{\mathrm{TN}}{\mathrm{FP+TN}}\right)\times 1 00=\left(\frac{\mathrm{Correctly\ detected\ no\ of\ control\ plants}}{\mathrm{Total\ number\ of\ control\ plants}}\right)\times 100 \tag{3}\]  
\[\mathrm{Accuracy}=\left(\frac{\mathrm{TP+TN}}{\mathrm{TP+FP+FN+TN}}\right) \times 100=\left(\frac{\mathrm{Total\ no\ of\ correctly\ detected\ plants}}{\mathrm{Total\ number\ of\ plants}}\right)\times 100 \tag{4}\]  
Figure 4: (a) Raw spectral data; (b) SNV-based preprocessed spectral data.  
where in this study, true positive (TP) is a positive response for a positive condition that indicates the number of correctly detected stressed plants. A false positive (FP) is a positive response for a negative condition that indicates the number of control plants identified as stressed plants. A false negative (FN) is a negative response for a positive condition, implying the number of stressed plants detected as control plants. True negative (TN) is a negative response for a negative condition, referring to the number of correctly detected control plants.
# Integration of Directional Antennas in an RSS Fingerprinting-Based Indoor Localization System
## 2 Related Work
In particular, in [52], the tag position was estimated from TOA and AOA measurements, the last one being estimated from the RSS received by an array of directional antennas. None of these works have analyzed the integration of directional antennas to enhance the performance of a room-based RSS fingerprinting-based ILS following the hypothesis laid down in this paper, _i.e._, the increment of the spatial variability of the RSS distribution through intrinsic directional antennas' spatial variability. As will be seen below, a classical room-level RSS-based positioning engine based on a simple MLP classifier will be employed to carry out the study. Omnidirectional and directional antennas have been integrated in order to analyze their effect on the system performance. In the next section, the architecture of the system is presented in detail.
###### Abstract  
Oscillations caused by the interaction between voltage source converters (VSCs) and weak grids are vital threats to the stability of power systems. Determining the appropriate parameters for the control of VSCs is essential to prevent the occurrence of oscillations in advance. To achieve this goal, a quantitative evaluation method of system stability for VSCs is proposed in this article to specify the stability boundary of control parameters. Then, an active damping controller for current control and a parameter optimization method for the phase-locked loop (PLL) is proposed, and the related parameters are designed based on the guidance of the proposed evaluation method. With planting the parameters optimization in the control of VSC, the stability boundary of control parameters of VSC is extended, so that the stability of VSC can be significantly improved. Finally, simulations are presented to verify the effectiveness of the theoretical analysis.  
voltage source converter; system stability; quantitative evaluation method; parameter optimization; stability boundary extended +
Footnote †: journal: Applied sciences
## 5 Conclusions  
A numerical study on the static and dynamic responses of 0-3 type IEPCM was carried out to investigate its orthotropic properties and lateral electromechanical driving capacities. The objective of this work is to develop an electromechanical driving theory and to provide experimental guidance for 0-3 type IEPCMs. The significant findings are summarized as follows.  
1. The maximum strain ratio at both ends of IEPCM in the \(x\)- and \(y\)-direction is 3.19, whereas that in the middle region is 2.69. These values indicate that good orthotropic characteristics and lateral piezoelectric driving capacity can be produced for 0-3 type IEPCMs.
2. The modal frequency distribution of the IEPCM is crowded together but with no repeated frequency; the vibration mode of the IEPCM is orthogonal, whereas the resonance frequency is 210.3 kHz. The harmonic responses of the IEPCM either in the \(x\)- and \(y\)-direction are almost the same at 420 kHz, which indicates some hysteresis characteristics exist. The load region of the driving voltage has a significant influence on the transient dynamic property of the IEPCM.
3. When the wafer width \(W_{\text{p}}\) of 0-3 type IEPCM is set at 1 mm, the main physical parameters of the optimal design for the IEPCM are achieved as follows: the thickness \(H\) is 0.62 mm, the electrode width \(W\) is 0.73 mm, the electrode spacing \(P\) is 1.02 mm and the corresponding volume \(V_{\text{TOT}}\) is 10.9 mm\({}^{3}\).  
**Acknowledgments: This work is supported by grants from the National Natural Science Foundation of China (Project No. 51578297, 51378270, 51678322 and 51650110509), the National\"111\" project, and the Taishan Scholar Priority Discipline Talent Group program funded by the Shan Dong Province.**Author Contributions:** [PERSON], [PERSON], [PERSON] and [PERSON] initiated the work; [PERSON] and [PERSON] were involved in the numerical analysis; [PERSON] and [PERSON] made the preliminary draft; [PERSON], [PERSON], [PERSON] and [PERSON] made the revisions and discussions.**Conflicts of Interest:** The authors declare no conflict of interest. The following abbreviations are used in this manuscript:  
PCM Piezoelectric cement-based material IEPCM Piezoelectric cement-based material with interdigitated electrodes SHM Structural health monitoring PZT Lead-zirconate-titanate  
Figure 7: The changes in the material volume of IEPCM vs. iteration numbers.
# Mars to Earth Data Downloading: A Directory Synchronization Approach
## 1 Introduction
This scenario is much more complex than the basic Martian scenario studied in [16], and was built by merging inputs form ESA/ESOC with the experience of other partners of the project, including GMV, DLR, Solenix and University of Bologna. Even in the simplified version considered here, this scenario is quite representative of future interplanetary environments, which is essential to draw general conclusions about the real applicability of the proposed approach in space. Experiments were carried out using a virtual testbed consisting of a few Linux based virtual machines (VMs), running ION, the BP implementation developed and maintained by NASA-JPL-Performance was assessed largely by the analysis of \"status reports\" [5], i.e., informative bundles optionally generated by DTN nodes to track bundle transmissions, and data file creation logs (for DTNbox). The results achieved are discussed in detail in the paper. The rest of the paper is organized as follows: in Section 2 we present a brief summary of DTNbox in Section 3 we describe the space scenario in the experiments; in Section 4 the direct and indirect approaches are compared, with explicit reference to this scenario; experiments are described in Section 5, and results analyzed in Section 6. Conclusions are finally drawn in Section 7.
# Temperature Sensing with Nd\({}^{3+}\) Doped YAS Laser Microresonators
## 4 Results and Discussion
### Laser Emission  
Neodymium behaves as a four-level system because it has two non-radiative and one radiative transition in the \({}^{4}\)F\({}_{5/2}\)\(\rightarrow\)\({}^{4}\)F\({}_{3/2}\)\(\rightarrow\)\({}^{4}\)I\({}_{11/2}\)\(\rightarrow\)\({}^{4}\)I\({}_{9/2}\) de-excitation process. When population inversion occurs (i.e., the number of ions in the \({}^{4}\)F\({}_{3/2}\) level is larger than in the \({}^{4}\)I\({}_{11/2}\) level), and the resonator internal gain is greater than the passive losses, the \({}^{4}\)F\({}_{3/2}\)\(\rightarrow\)\({}^{4}\)I\({}_{11/2}\) transition produces multimode laser emission at the 1064 nm band, as observed in Figure 6a.  
By analyzing the behavior of the most intense peak (1072 nm) as a function of the pumping power (shown in Figure 6b), the laser threshold power was determined to be 100 mW.  
It is possible to reduce the laser threshold by changing the wavelength of the pumping laser to 808 nm. Neodymium ions absorb more efficiently at this wavelength than the 532 nm employed. Moreover, the 808 nm radiation directly connects the \({}^{4}\)I\({}_{9/2}\) and \({}^{4}\)F\({}_{5/2}\) levels, decreasing the losses due to non-radiative de-excitation mechanisms. A decrease of at least about 34% in the laser threshold is estimated.
## 3 Prototype Description
### Global Stability
Then, it is possible to represent the values in a boxplot chart as shown in Figure 4, where the horizontal axis represents the solutions with different numbers of clusters and the vertical axis represents the ARI index value. The box shapes represent 50% of the values and the outer dashes represent the other 50%. Outliers are represented by circles outside the outer part, and the orange dash indicates the average of the values. With this graph, a view of the stability of each solution with \(k\) clusters is obtained. The ARI tends to decrease as \(k\) is increased, indicating a greater variation in the possible differences between the clusters of each solution. Analyzing the boxplot, after six clusters, the ARI value between solutions constantly varies negatively. Therefore, solutions with four, five, and six clusters become viable, since they have desirable stability in relation to solutions with \(k\) larger numbers, and still, allow for a more detailed analysis of each cluster. \(k=3\) was not considered because it has few clusters, aggregating different customers in the same group, making the solution more generalized and with few discernible details in each cluster.
# Assessing Abrasion Resistance in Concrete Pavements: A Review
## 4 Factors Affecting Abrasion Resistance
### Influence of Material Properties
#### 4.1.2 Fine Aggregates  
The abrasion resistance is also dependent on the quality of the fine aggregates or sands used. The use of crushed fine aggregate material showed increased abrasion resistance compared to natural sand aggregate [41]. In general, as the fraction of microfine increases, the flexural strength, freezing resistance, abrasion resistance, and compressive strength all tend to increase to certain limits, which then drop since there is not enough paste left to cover the aggregate. Adding 20% more rock microfine will increase the concrete's strength and resistance to abrasion. Sand with high roughness and low crushing values can be used to increase flexural strength and abrasion resistance. The abrasion resistance of concrete is unaffected by the silicon content of sand [42]. A previous study carried out on the influence of fine particles on the abrasion resistance and compressive strength of concrete showed that the use of different granulometric compositions of fine particles had no detrimental effects on the abrasion resistance of concrete. Additionally, it was verified that quartz sand improved abrasion resistance [19].  
The use of high-volume class F fly ash as a partial replacement of cement for various percentages was studied by [43], with the results showing higher abrasion resistance at older ages. Although the study did not specifically address the abrasion resistance of the  
Figure 6: The relationship between Los Angeles abrasion index and compressive strength of aggregate rocks [39].  
concrete mixes studied, it did remark that adding fly ash as a partial substitute for fine aggregate generally improves the strength characteristics of concrete. Better resistance to abrasion may result from this enhanced strength, particularly higher flexural strength [44].