esa-sceva/satcom-chunk-collection huggingface dataset:
# Fuzzy-Based Control System for Solar-Powered Bulk Service Queueing Model with Vacation
## 4 Results and Discussion
### The Mamdani-Type Fuzzy Inference Process
#### 4.4.2 Apply Fuzzy Operator  
When a Fuzzy Inference System includes multiple input variables, the antecedent of an If-Then rule is typically composed of multiple fuzzy linguistic sets, with each input variable contributing a corresponding fuzzy set to determine its degree of membership. In the solar power system example, the antecedent of Rule 2 includes two fuzzy linguistic sets: \"solar irradiance is high\" and \"battery charge level is low\". To process this rule, a fuzzy operator is applied to merge the membership values of these two sets, yielding a single numerical value that represents the overall result of the antecedent.  
The most commonly used fuzzy operators are the AND and OR operations, which are typically implemented using the min and max functions, respectively. While alternative methods such as product and probabilistic OR can also represent these operations, the min and max functions are preferred for their simplicity and efficiency. For instance, as illustrated in Figure 6, the AND operation is performed using the min function. In Rule 2, the membership values of the two fuzzy sets are 0.66 and 0.33, and the min function selects the lower value, 0.33, as the final result of the antecedent.
## 1 Introduction
The analysis is performed using vibration signals from sensors mounted on the structure, evaluating the power spectral density (PSD) and continuous wavelet transform (CWT) to capture changes in the natural frequency content. * The structural response under three harmonic excitations with frequencies near the natural frequencies is evaluated to identify the changes in the dynamic behavior of the damaged structure. For this purpose, the variations in spectral power and time-frequency energy associated with both the excitation and sensor location are compared. This document is organized as follows. Section 2 describes the experimental setup, the seven vibration scenarios, and the PSD and CWT methodologies. Section 3 presents and analyses the results from both methods. Finally, Section 4 reports the main conclusions and outlines directions for future work. The main contributions of this paper are listed as follows:  
* Evaluation of natural frequencies under ambient vibration excitation, transient excitations, and harmonic excitations through a comparison of PSD and CWT methods. * Proposal of distinct analysis for energy concentration, specifically using statistical metrics, the energy accumulated over time for each frequency, and the total energy delivered by each sensor. * Extraction of damage identification features using the proposed methods, considering the placement of the sensors on the structure.
## 2 Data Description  
For this study, a 5.384 sq. kilometres test area belonging to the State Reserve Fund was selected in the village of Privolnoye, which is located within the 48.5\({}^{\prime}\) E 39.2\({}^{\prime}\) N provincial borders of Jalilabad, Republic of Azerbaijan as shown in Figure 1a,b,d. The low-mountainous semi-arid area is planted with durum winter wheat.  
Durum wheat differs from soft wheat in signs, appearance, and other signs. The spikes of durum wheat are very thick and tough. The grains vary in different colours such as white, red, light, or dark amber. The bays are long as the spike, upright. The grains do not fall on the ground when they are ripe as they are tightly covered with external and internal flower scales. the number of chromosomes in the plant is 28. Because durum wheat flour has better quality than flour of soft wheat, it is used in pasta production more [21].  
The atmospheric factors demanded for the first period of the development of winter wheat crops are different from spring wheat. Winter wheat has a longer vernalization period [21].  
Figure 1: Details of the test area. (**a**) Economic regions of Azerbaijan; (**b**) Natural Red Blue Green (RGB) band combination of Jalilabad, test area highlighted; (**c**) 2021–2022 NDVI time series analysis of the study area, created using Google Earth Engine (GEE), (**d**) Natural Red Blue Green (RGB) band combination of the test area highlighted.  
Winter wheat in Azerbaijan is mainly planted during the period of late September to early November and then harvested by the end of May or the beginning of June of the next year. Winter durum wheat usually is not irrigated in semi-arid high-productivity areas, thus, the water consumption balance model for yield estimation is applicable based on the soil moisture [21].  
Vegetation analysis was applied to imagery, which was ingested during the springtime, April 2022, while soil quality indices such as Soil Texture Index (STI), Normalized Difference Moisture Index (NDMI), Total Inorganic Carbon Index (TICI), Soil Salinity Index (SI) were calculated based on the previous timeline, August 2021.  
Time Series Analysis of the NDVI values during a year retrieved from GEE. Using the information mentioned in Figure 1c, bare soil and high vegetation periods were defined. Between the time periods of early August 2021 and late September 2021, the vegetation trends are following the lowest stable values.  
Optic and radar data combinations such as Sentinel 2 and SRTM were applied. Two level-2 Sentinel 2 products covering bare soil before planting from 23 August 2021, and green fields of the tillering winter wheat from 15 April 2022, were obtained as shown in Figure 2.  
30 m-resolution SRTM DEM data (N39E048) was obtained using the SRTM Downloader plugin in Quantum Geographic Information System (QGIS) as shown in Figure 3.  
Figure 2: The Area of Interest (AOI) Sentinel-2 raster data as (**a**) RGB combination, bare soil; (**b**) False Infra-Red (FIR) combination, bare soil; (**c**) RGB combination, green field; (**d**) FIR combination, green field.
# Removal of Cu(II) from Aqueous Solutions Using Amine-Doped Polyacrylonitrile Fibers
###### Abstract  
Polyacrylonitrile (PAN) fibers were prepared via electrospinning and were modified with diethylenetriamine (DETA) to fabricate surface-modified PAN fibers. The surface-modified PAN fibers were used to evaluate their adsorption capacity for the removal of Cu(II) from aqueous solutions. Batch adsorption experiments were performed to examine the effects of the modification process, initial concentration, initial pH, and adsorbent dose on the adsorption of Cu(II). Kinetic analysis revealed that the experimental data fitted the pseudo-second-order kinetic model better than the pseudo-first-order model. Adsorption equilibrium studies were conducted using the Freundlich and Langmuir isotherm models, and the findings indicated that the PAN fibers modified with 85% DETA presented the highest adsorption capacity for Cu(II) of all analyzed samples. Moreover, the results revealed that the Freundlich model was more appropriate than the Langmuir one for describing the adsorption of Cu(II) onto the modified fibers at various initial Cu(II) concentrations. The maximum adsorption capacity was determined to be 87.77 mg/g at pH 4, and the percent removal of Cu(II) increased as the amount of adsorbent increased. Furthermore, the surface-modified PAN fibers could be easily regenerated using NaOH solution. Therefore, surface-modified PAN fibers could be used as adsorbents for the removal of Cu(II) from aqueous solutions.  
polyacrylonitrile; electrospun fibers; Cu(II) removal; batch experiments; adsorption models
## 4 Proposed Hybrid FVIMDE Algorithm
### Exploration and Exploitation Behavior
#### 4.6.2 Exploitation Behavior  
Exploitation, on the other hand, refers to the algorithm's ability to intensively search within promising regions of the search space that have already been identified as potentially containing optimal solutions. This behavior is essential for refining solutions and converging toward the global optimum with high precision. In the Hybrid FVIMDE Algorithm, exploitation is primarily managed during the Four Vector Intelligent Mechanism (FVIM) phase.  
The FVIM phase leverages the best solution found during the DE phase as a starting point for further refinement. The position updates in this phase are guided by the influence of the Alpha, Beta, Gamma, and Delta particles, with a particular emphasis on the alpha particle, which represents the best solution. The balance between exploration and exploitation is controlled by the alpha particle's dominant role, ensuring that the search remains focused on the most promising regions. Additionally, the process involves a controlled reduction in the exploration range, governed by a linearly decreasing coefficient \(a\), as described in Equation (10). As iterations progress, the influence of the alpha particle becomes increasingly dominant, while the random perturbations decrease. This narrows the search to a smaller region of the search space, focusing on refining the best solutions identified so far.
# Scheduling of Industrial Control Traffic for Dynamic RAN Slicing with Distributed Massive MIMO +
## 8 Conclusions  
Our work in this paper allows the scheduling of industrial control traffic to be combined with other traffic types through network slicing in either centralized or distributed massive MIMO (also known as LIS) systems. This is highly desirable in an industrial setting, where industrial control traffic must co-exist with other traffic such as from (industrial) Internet of Things sensors, lower-priority data collection for diagnostics and analytics, and staff data traffic. Our optimization formulations are suitable for dynamic RAN slicing, in which other slices can borrow resources from the industrial control traffic when not in use.  
We have presented new optimization methods, based on mixed-integer programming, for scheduling industrial control traffic in a dynamic RAN slicing context. We have then evaluated our optimization methods using numerical experiments. We showed that we could schedule both periodic, deadline-based control traffic alongside background traffic, meeting the deadline constraints of the former while satisfying the required data rates for the latter, while minimizing the use of radio resources in terms of pilot signals. Moreover, we could solve our optimization problems quickly, within one second in all cases tested, for up to 32 scheduled nodes, with varying traffic profiles. This makes our methods feasible for use in real-world settings, since the solution time is much shorter than the time over which a computed schedule would be used.  
In future work, this research would benefit from larger-scale studies. So far, we have tested scenarios up to 32 nodes as we explored the performance of the different optimization formulations tested. However, we have now identified which methods are the most efficient with the lowest solution times, paving the way to try scenarios with larger numbers of nodes. As well as this, there is further work to be done in refining how the parameters used as input to the optimization problems are determined, in particular, the maximum frame length and maximum number of pilots. Another direction for future work is to implement our methods in an LIS test to conduct more realistic evaluations.  
Conceptualization, E.F. and M.P.; Methodology, E.F. and M.P.; Software, E.F.; Validation, M.P.; Formal analysis, E.F. and M.P.; Investigation, M.P.; Writing--original draft, E.F. and M.P.; Writing--review & editing, E.F. and M.P.; Visualization, E.F. All authors have read and agreed to the published version of the manuscript.  
The work of [PERSON] was supported by the REINDEER project of the European Union's Horizon 2020 research and innovation program under grant agreement no. 101013425, the SSF project SEC4 FACTORY under grant no. SSF RIT17-003, and the Celtic-Next project IMMI-NENCE. [PERSON] is a member of the Excellence Center at Linkoping-Lund on Information Technology (ELIIT).  
The data presented in this study are available in this article.  
The authors declare no conflicts of interest.
## 3 Results
### ANOVA, Analysis of Variance  
The derived confidence levels of factors attributed to the virtue of the gamma camera scan were obtained in Table 4. The importance of the dominant factors of the gamma camera scan protocol was analyzed by executing an \(F\)-test (cf. Equation (6)). A dominant factor was defined as providing high contribution. Therefore, factors A (collimator) of 38.5% and B (DTD) of 41% were accredited as the most affective to the imaging quality of the V-shaped slit gauge. Moreover, no factors from A to F could be aligned separately to assure high rank because four out of six factors (A, B, E, and F) were significant according to F-test, although factor E (Matrix size) or F (Zoom of ROI) showed a minor contribution (1.4% and 4.0%, respectively, cf. Table 4).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Factor** & **SS** & **DOF** & **Contribution** & **Var** & **F** & **Probability** & **Confidence Level** & **Significant \({}^{*}\) \\ \hline A & 1694.91 & 1 & 38.5\% & 1694.9 & 463.42 & 0.0\% & 100.0\% & Yes \\ B & 1784.53 & 2 & 41\% & 892.3 & 243.96 & 0.0\% & 100.0\% & Yes \\ C & 17.20 & 2 & 0.4\% & 8.6 & 2.35 & 9.9\% & 90.1\% & No \\ D & 2.68 & 2 & 0.06\% & 1.3 & 0.37 & 69.4\% & 30.6\% & No \\ E & 55.46 & 2 & 1.4\% & 27.7 & 7.58 & 0.07\% & 99.9\% & Yes \\ F & 176.64 & 2 & 4.0\% & 88.3 & 24.15 & 0.0\% & 100.0\% & Yes \\ Others & 110.41 & 6 & 2.5\% & 18.4 & 5.03 & 0.01\% & 100.0\% & Yes \\ Error & 526.67 & 144 & 12.1\% & 3.7 & & & \(\text{S}=1.91\) & \\ Total & 4368.49 & 161 & 100\% & & & & & \\ \hline \hline \end{tabular}  
* Note: At least 99% confidence level.  
\end{table}
Table 4: The confidence levels of factors are related to the gamma camera scan protocol contributions. One factor is defined as significant if the confidence level reached 99%.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Group** & \multicolumn{3}{c}{**Radiologist-1**} & \multicolumn{3}{c}{**Radiologist-2**} & \multicolumn{3}{c}{**Radiologist-3**} & _Ave._ & _Sd_ & _S/N_ \\ \hline
1 & 16 & 18 & 18 & 11 & 17 & 14 & 18 & 15 & 18 & 16.11 & 2.42 & 15.78 \\
2 & 18 & 17 & 17 & 16 & 16 & 16 & 15 & 17 & 17 & 16.56 & 0.88 & 21.88 \\
3 & 13 & 16 &
## 4 Discussion
Our study had very similar findings on the basis of which we can consider the power chain as a material that fulfilled most of the requirements for dental trauma splints. The PCS was easy and fast to apply and remove. The participants found the PCS significantly more esthetic compared to other materials assessed. It was very well tolerated, causing, like two other materials, only a slight discomfort of the teeth while wearing and a mild impairment of speaking and eating mainly on the first day after application [31]. The power chain did not traumatic surrounding soft tissues, which is important for healing of injured structures as well as the maintenance of proper oral hygiene. Both the volunteers' subjective findings and SBI/API results revised that all three materials did not impair the oral hygiene. Dental trauma splints, due to their designs, may be the retention factors for plaque accumulation and compromises plaque control. The oral cavity, next to the external environment at the site of the injury, is the main source of bacteria that may act as an inflammatory stimulus. The bacteria have the ability to migrate down the blood clot that forms in the periodontal ligament [32]. The constructions of the splints we tested did not hinder proper oral hygiene and did not contribute to the development of an inflammatory process. It is noticeable that, in case of all three splints, we recorded more pronounced reduction of the central incisors' mobility following splint application compared to the lateral incisors. As the authors of another study have already mentioned, such results are clinically relevant as the central incisors, representing the injured teeth, required stability after splinting while the lateral incisors served as neighboring intact, firmly fixed in the socket teeth [5]. On the basis of the assessment of the power chain splint conducted in both studies, the one done in a laboratory situation [18] and ours, we can conclude that the power chain is a good alternative for injured teeth splinting. Application of the power chain benefits both the patient and the dentist. With its characteristics, such as stiffness, good esthetics, or low price, it was compared to a monofilament nylon fishing line [18]. However, due to its small diameter, the application of a nylon splint can sometimes be problematic. According to these authors, a splinting technique with monofilament nylon line cannot be used in case of the presence of edentulous spaces [27]. We had some doubts as to whether the power chain is suitable for using in such situations. However, applying it clinically in injured patients with some missing teeth has resulted in good treatment effects so far (Figure 8). However, further research on this aspect is needed. One limitation of this study is that the mobility was measured on the teeth with healthy periodontal ligaments, bone, and other teeth structures not damaged due to trauma. Nevertheless, the definite advantage of this study is the use of vital teeth with their complex structures of the biological tissues. The results of the present study will justify using it in trauma patients.
# Audio Anti-Spoofing Based on Audio Feature Fusion
###### Abstract  
The rapid development of speech synthesis technology has significantly improved the naturalness and human-likeness of synthetic speech. As the technical barriers for speech synthesis are rapidly lowering, the number of illegal activities such as fraud and extortion is increasing, posing a significant threat to authentication systems, such as automatic speaker verification. This paper proposes an end-to-end speech synthesis detection model based on audio feature fusion in response to the constantly evolving synthesis techniques and to improve the accuracy of detecting synthetic speech. The model uses a pre-trained wav2 vec2 model to extract features from raw waveforms and utilizes an audio feature fusion module for back-end classification. The audio feature fusion module aims to improve the model accuracy by adequately utilizing the audio features extracted from the front end and fusing the information from timeframes and feature dimensions. Data augmentation techniques are also used to enhance the performance generalization of the model. The model is trained on the training and development sets of the logical access (LA) dataset of the ASVspoof 2019 Challenge, an international standard, and is tested on the logical access (LA) and deep-fake (DF) evaluation datasets of the ASVspoof 2021 Challenge. The equal error rate (EER) on ASVspoof 2021 LA and ASVspoof 2021 DF are 1.18% and 2.62%, respectively, achieving the best results on the DF dataset.  
deep learning; wav2 vec 2.0; automatic speaker verification; deep-fake detection; ASVspoof Challenge
ECG is the most reliable tool for interpreting MI [12; 13; 14], apart from the emergence of expensive and sophisticated alternatives [7]. However, interpreting MI via morphological ECG is a challenging task due to its significant variation in different patients under different physical conditions [15; 16]. To prevent the misinterpretation of MI diagnosis, a study uses the nature of ECG signals in a sequence model is automatically necessary. The sequential model consists of sequences of ordering events, with or without concrete notions of time. The algorithm that is usually used for sequential models is a deep learning technique [17]. Some deep learning algorithms that used the sequential model to interpret MI from ECG signals have been presented in References [12; 14]. These studies combine Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) architecture to interpret MI only in one (Lead I) or several leads (I, II, V1, V2, V3, V4, V5, V6). A sequence modeling is synonymous with recurrent networks that maintain a vector of hidden activations that are propagated through time for most deep learning practitioners [17]. Basic recurrent network architectures are notoriously difficult to train due to the large increase in the norm of the gradient during training, and the opposite behavior when long term components go exponentially fast to norm zero [18]. Some elaborate architectures are commonly used instead, such as the LSTM [19] and GRU [20; 21]. Other architectural innovations and training techniques for recurrent networks have been introduced and continue to be actively explored [22; 23; 24; 25]. Unfortunately, none of these studies suggested which recurrent network is a suitable method for classification. In the present paper, three sequence model classifiers to classify MI and healthy control of 15-lead ECG signals are discussed. The comparison of the recurrent network algorithms is proposed to automatically interpret MI via ECG signal. The recurrent network classifiers include Recurrent Neural Network (RNN), LSTM, and GRU. The objective is to obtain the optimum sequence model in ECG signal recording. To evaluate the performance of recurrent network classifiers in a sequence model, the metric evaluation is proposed. This study also analyzes classifier performance in imbalanced data that the sample size of the data classes is unevenly distributed, among the class of MI and cardiac normal in healthy control patients [26]. In such situations, the classification method tends to be biased towards the majority class. Therefore, this paper uses metric performance balanced accuracy (BACC) and Matthew's Correlation Coefficient (MCC) to produce better analysis in imbalanced data of MI [26]. In some studies, the use of leads is an important factor for determining the performance results of classifiers [12; 13]. The sequence model classifier can be used for 15-lead ECG instead of only use for one or several leads.
# Modeling and Analysis of Staged Constellation Deployment from a Single-Unit System
## 2 Defining the Deployment Problem
### Other Considerations  
The initial relative motion is defined as \(\left[\dot{\theta}\ \dot{\theta}\right]_{0}^{T}\), where \(\theta_{0}\) is the initial separation angle, and \(\dot{\theta}_{0}\) is its rate of change. These values typically depend on the deployment strategy imposed upon the launch vehicle, which defines the deployment direction, frequency, and velocity, as seen in the QB50 constellation [16]. However, unless multiple many-in-one systems are launched, this deployment sequence becomes less critical, as the proposed disengagement sequence inherently determines and controls initial relative motion.  
However, orbital elements at deployment still impact differential drag phasing and phase times due to variations in atmospheric density. Mean atmospheric density is influenced by altitude, eccentricity, and solar activity. Lower altitudes and circular orbits experience higher densities, while atmospheric density at a given altitude increases with solar activity. Since dynamic pressure directly affects \(\dot{\theta}\) through differential drag, optimizing phasing times requires launching at lower altitudes, in circular or quasi-circular orbits, and during periods of high solar activity.
## 4 Implementation  
### Implementation  
#### Implementation Setup  
The specification of our implementation setup is shown in Table 1. The hardware environment consists of several components, including a CPU, a GPU, 8 GB of RAM memory, 110 GB of storage, and the network interface card (NIC). On the software side, the system runs CentOS 7.6.1810 (64-bit) as the operating system. The development environment is based on PyTorch version 1.12.1, with Python 3.9 as the primary programming language.