esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimal Placement of Multiple Feeder Terminal Units Using Intelligent Algorithms
## 3 Mathematical Model
### Objective Function
#### 3.2.2 Maintenance Cost  
The \(C_{mt}\) of distribution FTU configuration scheme refers to the cost of FTUs failure maintenance and regular maintenance, which is a continuous cost in the life cycle of terminal unit equipment. The current method of cost management for maintenance and overhaul of distribution network equipment adopted by most utilities is a percentage of the initial total investment cost, which can be calculated by:  
\[C_{mt}=\eta C_{inv} \tag{9}\]  
where \(\eta\) is the proportion of maintenance cost compared to the initial total investment cost.
# Towards Improving the Durability and Overall Performance of PV-ETICS by Application of a PCM Layer
## 3 Initial Investigations and Problem Definition  
This study is devoted to identifying the overheating problem of PV-ETICS during use in different climatic conditions across Europe. The study described in Section 2 (see Figure 3) shows the differences in weather parameters, mainly in the intensity of solar radiation. The initial thermal analyses show that, for selected locations, the thermal behaviour of ETICS and PV-ETICS was determined using a simulation technique. The results of extreme temperatures for both systems are presented in Figure 5. It can be noted that the temperature of traditional ETICS does not exceed 60 \({}^{\circ}\)C. On the other hand, the peak temperature of PV-ETICS, which is covered on the outside by a dark PV layer, is over 80 \({}^{\circ}\)C for all locations. For some specific cities like Rome, Bucharest and Madrid, the value is well above 90 \({}^{\circ}\)C. It can be concluded that there is a risk of overheating, causing possible destruction of PV panels and limits to service life. Therefore, eliminating this risk is crucial to the proper design of En-ActivETICS, which is the modification of PV-ETICS by PCM application. One of the justified solutions is the implementation of a PCM layer on the inner side of PV panel. The size and material properties of this layer should be initially determined based on the results from, e.g., numerical simulation. The following three-step approach is proposed:  
Figure 4: Solar irradiance availability at the horizontal surface for analysed locations.  
Step 1--Determination of the daily average and maximum amount of absorbed solar energy to be stored in the PCM layer:  
* determination of the amount of solar energy received by the panel when the temperature is above 80 \({}^{\circ}\)C;
* determination of the amount of energy converted into electricity;
* determination of the daily sum of solar energy converted into heat.  
Step 2--Determination of required the amount of phase change material as well as the total weight and thickness of the layer.  
Step 3--Investigation of the effect of latent heat storage on the temperature of the PV panel through calculation the values of t80 (number of overheating hours when temperature is above 80 \({}^{\circ}\)C) and DH80 (degree hours of overheating above 80 \({}^{\circ}\)C) for ETICS and En-ActivETICS.
# Origin and Function of Structural Diversity in the Plant Specialized Metabolome
## 4 Decoration and Conjugation in Specialized Metabolism  
### Diversity of Decorations  
The majority of the structural diversity in the plant specialized metabolome arises from \"decorative\" biosynthetic steps, such as hydroxylations, methylations, glycosations, acylations and prenylations [126]. The number of different modifications is rather limited and the nature of the decorations for different classes of specialized metabolites is often the same. For example, classes as far apart as flavonoids, indole alkaloids and apocarotenoids are all decorated with glucose and with malonylglucose [1; 3]. In the specialized metabolite profile of a plant extract, the common decorations are reflected in an overrepresentation of certain mass differences corresponding to the difference between two core structures. For example, in the maize specialized metabolome, the mass difference of 108.021 Da, corresponding to the difference between a phenylpropanol and a flavonoid core structure, is observed between \(p\)-coumaric acid and naringenin, but also between caffeoyl hexose and eriodictyl 7-O-hexoside (hydroxylation and addition of a hexose to both) or between sinapoyl hexose and methoxy-homoeriodictyl (two methylations of both), as well as between dihydroxyindole-3-acetic acid (caffeoyl) hexoside and dihydroxyindole-3-acetic acid (eriodictyl-O-) hexoside (hydroxylation and addition of dihydroxyindole-3-acetic acid and hexose to both) [6]. The majority of plant specialized metabolites are glycosylated and/or acylated, with acylations being aromatic (\(p\)-coumaric, caffeic, ferulic, sinapic, gallic or \(p\)-hydroxybenzoic acids) or aliphatic (malonic, acetic, malic, succinic or oxalic acids). Because of the multitude of different possible decoration sites (hydroxyl groups), and the multitude of possible combinations of decorations, the number of possible structures grows exponentially with each added decoration [127]. Moreover, N-acylation of alkaloids, such as anthranilic acid or polyamines, leads to the formation of a high variety of hydroxycinnamic acid amides [128]. Intramolecular cyclization through phenol-oxidative coupling between the phenolic rings of two \(p\)-coumaroyl substitutes in bis-(\(p\)-coumaroyl) polyamines is the biosynthetic mechanism leading to cyclic alkaloids such as lunarine (in _Lunaria annua_ seeds) and aphelandrine (in _Aphelandra_ sp. roots) [129; 130]. The introduction of phenolic rings through aromatic acylation allows, thus, further structural diversification through oxidative coupling.
###### Abstract  
With the growth of the plant-based food sector, increasing amounts of by-products are generated. Sea buckthorn pomace (SBP), a by-product of juice and other manufacturing products, is rich in bioactive compounds such as phenolics, oligosaccharides, proteins, and dietary fiber. The aim of the study was to evaluate the impact of modification methods, such as enzymatic hydrolysis and supercritical carbon dioxide extraction (SFE-CO\({}_{2}\)), on the chemical composition and technological properties of SBP. SBP and SBP obtained after SFE-CO\({}_{2}\) (SBP-CO\({}_{2}\)) were enzymatically modified using Pectinex\({}^{\circledown}\) Ultra Tropical, Viscozyme\({}^{\circledown}\) L, and Celluclast\({}^{\circledown}\) 1.5 L (Novozyme A/S, Bagsvaerd, Denmark). The SBP's main constituent was insoluble dietary fiber (IDF), followed by crude proteins and lipids (respectively, 58.7, 21.1 and 12.6 g/100 in d.m.). SFE-CO\({}_{2}\) reduced the lipid content (by 85.7%) in the pomace while increasing protein and TDF content. Enzymatic hydrolysis decreased the content of both soluble dietary fiber (SDF) and IDF, and increased the content of mono- and oligosaccharides as well as free phenolics, depending on the commercial enzyme preparation used in SBP and SBP-CO\({}_{2}\) samples. Celluclast\({}^{\circledown}\) 1.5 L was the most effective in hydrolyzing IDF, while Viscozyme\({}^{\circledown}\) L and Pectinex\({}^{\circledown}\) Ultra Tropical were the most effective in degrading SDF. Enzymatic treatment improved water swelling capacity, water retention capacity, water solubility index, oil retention capacity of SBP and SBP-CO\({}_{2}\); however, it did not have a significant effect on the stability of the emulsions. Modification of SBP by SFE-CO\({}_{2}\) effectively increased WSC and WSI, however it reduced WRC. These findings highlight the potential of targeted modifications to enhance the nutritional and technological properties of SBP for functional food applications.  
sea buckthorn pomace; enzymatic hydrolysis; dietary fiber; oligosaccharides; technological properties +
Footnote †: journal: Applied Sciences
# Characterization of the Intelligibility of
###### Abstract  
The purpose of this study is to characterize the intelligibility of a corpus of Vowel-Consonant-Vowel (VCV) stimuli recorded in five languages (English, French, German, Italian and Portuguese) in order to identify a subset of stimuli for screening individuals of unknown language during speech-in-noise tests. The intelligibility of VCV stimuli was estimated by combining the psychometric functions derived from the Short-Time Objective Intelligibility (STOI) measure with those derived from listening tests. To compensate for the potential increase in speech recognition effort in non-native listeners, stimuli were selected based on three criteria: (i) higher intelligibility; (ii) lower variability of intelligibility; and (iii) shallower psychometric function. The observed intelligibility estimates show that the three criteria for application in multilingual settings were fulfilled by the set of VCVs in English (average intelligibility from 1% to 8% higher; SRT from 4.01 to 2.04 dB SNR lower; average variability up to four times lower; slope from 0.35 to 0.68%/dB SNR lower). Further research is needed to characterize the intelligibility of these stimuli in a large sample of non-native listeners with varying degrees of hearing loss and to determine the possible effects of hearing loss and native language on VCV recognition.  
intervocalic consonants; hearing screening; Short-Time Objective Intelligibility (STOI); speech intelligibility; speech-in-noise testing Article
## 4 Experimental Results  
### Evaluation Data  
We use four well-established, public, emotion benchmark datasets. Tales is the most relevant public benchmark dataset for our work. It is an affect dataset with 1207 sentences built from literature, namely fairy tales by [PERSON], [PERSON], and the [PERSON] [7]. It was annotated with the six basic Ekman emotions; however, anger and disgust were merged into a single label. GoEmotions [13] is the second most relevant public benchmark dataset for our work. It is a fine-grained emotion dataset consisting of 54,260 Reddit posts annotated by crowd workers with 27 emotions plus a neutral label. ISEAR (International Survey on Emotion Antecedents and Reactions) [25] is a collection of 7666 sentences each answering a question assigned to 7 emotions: joy, fear, anger, sadness, disgust, shame, and guilt. EMOINT [62] contains crowdsourced annotations for 7102 tweets annotated with anger, joy, sadness, and fear. The Tales dataset is relevant because it built from literature. GoEmotions is relevant because it is annotated with fine-grained emotions. We included ISEAR and EMOINT as benchmark datasets in these experiments because they are commonly used in related work. Both are also part of UnifyEmotions [11] and are thus easily accessible.  
In addition to the public benchmark datasets, we also manually annotated a subset of our dataset with our own taxonomy. This is a set of 727 sentences annotated with all 38 emotions and detailed in Table 4 under the header \"Gold\". It was created by iterative stratified sampling from the original dataset obtained in Section 2.5. A total of 1000 sentences were originally extracted, but to reduce the annotation work, 100 expected neutrals were discarded, and ultimately only the first 727 examples were manually labeled (labeling was performed by the first author).
## 1 Introduction
Therefore, enhancing the robustness to noise and increasing the communication distance represent two of the main challenges for automotive VLC systems developers [2]. To address these issues, an adaptive logarithmic transimpedance circuit is proposed instead of the classical linear approach. As far as we know, this is the first intensive experimental investigation of such a concept in outdoor VLC applications. The simulation results and the experimental tests have confirmed that in comparison with the linear transimpedance circuit, the adaptive logarithmic approach provides the VLC receiver with an extended dynamic range and improved robustness to photoelectron saturation, in accordance with the requirements of automotive applications [2]. Thus, the integration of the logarithmic transimpedance circuit enables usage of VLC sensors in automotive applications not only in favorable conditions but also problematic circumstances. Usage of the logarithmic transimpedance circuit for VLC sensors has been partially addressed by our group in [23]. However, unlike in [23], this article provides the results of extensive simulation and experimental testing and evaluation that confirm the performances of the prototype, more insights on the concept, and details on the benefits and applications. The rest of this article is structured as follows: Section 2 addresses the issues associated to the usage of the VLC technology in vehicular applications and presents some of the existing solutions to mitigate them. Section 3 introduces the concept of adaptive logarithmic transimpedance and presents the advantages associated with its usage in automotive VLC applications. Section 4 summarizes the results of the simulation and of the experimental evaluation of a logarithmic transimpedance circuit providing a comparison to the linear approach. Section 5 provides a discussion on the observationsthat can be drawn from the experimental evaluation and discusses the future perspectives in this direction, whereas Section 6 provides the final conclusions of this article, followed by a bibliography. Debate on the Usage of the Visible Light Communication Technology in Automotive Applications: Issues and State of the Art Solutions
## 4 Experiments and Results
### Simulation Results
#### 4.2.4 Comparison of Leading Autonomous Vehicle Experiments  
To evaluate the advantages of the proposed experiment, other experiments were executed--namely, the entire manual vehicle and leading manual vehicle experiments, as shown in Tables 4 and 5. Concerning mobility, the proposed experiment obtained the development of average speed compared with the entire manual vehicle experiment. As seen in Table 4, comparing the entire manual vehicle experiment, the 20% AV penetration rate case increased the average speed 1.19 times. Comparing the entire manual vehicle experiment, full automation traffic obtained an increase in the average speed of 1.27 times. Concerning the DRL policy, the proposed experiment obtained a development of average reward compared with the entire manual vehicle experiment. Additionally, comparing the entire manual vehicle experiment, the 20% AV penetration rate case increased the average reward 3.77 times. Full automation traffic obtained a development in the average reward of 11.64 times compared with the entire manual vehicle experiment. Therefore, the proposed experiment performed better than the entire manual vehicle experiment with respect to mobility and the DRL policy.  
Figure 9: The results of the measures of effectiveness (MOE) evaluation over autonomous vehicle (AV) penetration rates: (a) average speed vs. AV penetration rates; (b) fuel consumptions vs. AV penetration rates; and (c) emissions vs. AV penetration rates. Nox: nitrogen oxide; HC: hydrocarbons  
Furthermore, the proposed experiment is also compared to the leading manual vehicle experiment, as shown in Table 5. Concerning mobility, the proposed experiment obtained an increase in average speed compared with the leading manual vehicle experiment. The proposed experiment increased the average speed 1.07 times compared with the leading manual vehicle experiment. Concerning the DRL policy, the proposed experiment obtained a higher average reward compared with the leading manual vehicle experiment. Furthermore, the proposed experiment increased the average reward 1.10 times compared with the leading manual vehicle experiment. Therefore, the proposed experiment performed better than the leading manual vehicle experiment regarding mobility and the DRL policy.
## 4 Review of Existing Methods and Capabilities
### Path Planning
#### 4.1.4 Multiple Objective
A Lie group of flocks approach was used for the high-level supervisory control decision making. In related work, [PERSON] _et al._[85] provided an approach for distributed and cooperative decision making for collaborative electronic warfare. Similar algorithms were utilized with a focus on radar deception, ensemble tracking, and collision avoidance among the vehicles. Optimizing resources for multi-criteria decision making using ant colony optimization (ACO) and analytic hierarchy process (AHP) was proposed by [PERSON] _et al._[59]. Unlike other approaches that rank a finite set of alternatives in a multi-criteria decision making problem, this approach utilizes the ACO to obtain optimal solutions satisfying some of the path-planning criteria. The AHP is then used to select the best UASs to perform each portion of the mission, optimizing the results of the overall mission. This approach could be extended to numerous UAS problems and objective constructs. [PERSON] _et al._[86] proposed using a linkage and prediction dynamic multi-objective evolutionary algorithm in conjunction with a Bayesian network and fuzzy logic decision making process. Historical Pareto sets are collected and analyzed for the online path planning. A Bayesian network and fuzzy logic are then utilized for bias calculations for each objective. Results of using this method shows improved performance over completely restarting the path-planning algorithm at each objective change.
## 11 Support Vector Machine  
The SVM is a classifier that finds a rule that can separate groups of datasets for classification and regression tasks [26]. In complex and high-dimensional datasets, one of SVM's distinctive features is the ability to leverage kernel functions to handle nonlinear classification tasks. This work uses the Gaussian Radial Basis Function (RBF) kernel to find the hyperplane that separates the two classes. The primary objective of the SVM is to achieve maximum margin classification. The margin is the distance between the hyperplane and the nearest support vector of either class [27]. By incorporating the Gaussian kernel, the hyperplane formulation becomes the following:\[f(x)=\sum_{i=1}^{n}\alpha_{i}y_{i}K(x,x_{i})+b \tag{5}\]  
Therein, we have the following:  
* \(f(x)\) is the decision function.
* \(\alpha_{i}\) are the LaGrange multipliers corresponding to support vectors.
* \(x_{i}\) is the class label of the ith instance.
* \(K(x,x_{i})\) is the RBF kernel function measuring similarity between input vectors \(x\) and \(x_{i}\). It is the Gaussian kernel function.
* \(b\) is the bias term. The \(K(x,x_{i})\) kernel function is expressed as the following equation: \[K(x,x_{i})=\exp\left(-\gamma\cdot||x-x_{i}||^{2}\right)\] (6)  
Therein, we have the following:  
* \(\exp\) is the exponential function.
* \(\gamma\) is the negative gamma parameter controlling the shape of the decision boundary.
* \(\left(||x-x_{i}||^{2}\right)\) is the squared Euclidean distance between input vectors \(x\) and \(x_{i}\), thereby measuring their closeness in the input space.
## 2 Material and Methods
### Qualitative Analysis of Aroma Compounds
#### 2.4.2 SPME-GC-QTOF-MS  
Free form volatiles in the blueberry wines were measured using the headspace-solid phase microextraction (HS-SPME) method coupled with GC-QTOF-MS. A 50/30 \(\upmu\)m DVB/CAR/PDMS fiber (Supelco Inc., Bellefonte, PA, USA) was used for volatile extraction. One mL of wine sample was diluted with 9 mL of citrate buffer (0.2 M, pH 5.0) in a 20 mL vial, and 3 g of NaCl were added with a small magnetic stir bar. The vial was tightly capped and equilibrated at 50 \({}^{\circ}\)C in a thermostatic bath for 15 min and extracted by SPME fiber for 45 min at the same temperature with stirring (500 rpm). After extraction, the fiber was inserted into the injection port of GC (250 \({}^{\circ}\)C) to desorb the analytes. The extraction and desorption was conducted manually. The GC-QTOF-MS conditions were the same as described above, except the split ratio was 1:10, and no solvent delay was used. The extraction was performed in duplicate for each of the eight biological replicates (four replicates \(\times\) two varieties). The in-batch order of all samples analyzed in this study was randomized with one blank sample injection for every 5 samples.
## 5 Results and Use Case
### IT Infrastructure Onboarding to the SIEM
This information is crucial for effective incident response and remediation efforts;
* Integration with Other Security Tools: Many SIEM systems can integrate with other security tools, such as threat intelligence platforms and automated response solutions, to enhance threat detection capabilities and streamline the incident response process.
# Feature-Selection-Based DDoS Attack Detection Using AI Algorithms
## 5 Experiment Result
### Performance Parameters  
We use a variety of metrics, including F1, recall, accuracy, precision, and a confusion matrix, to determine how well AI models perform. Accuracy (shown in Figure 3, precision, recall, and F1 scores are shown in Figure 4 and results are in Table 3.  
Figure 4: Recall, precision, and F1_Score.  
Figure 3: Accuracy of all models.  
**Accuracy**: One of the performance indicators is accuracy, which is mathematically defined as a fraction where the denominator specifies the sum of false positives and negatives as well as the terms present in the numerator, and the numerator specifies the sum of true positives and true negatives. Equation (4) defines it as follows:  
\[\text{Accuracy}=\left((\text{TP}+\text{TN})/(\text{FP}+\text{FN})\right)^{*}100, \tag{1}\]  
where TP = True Positive, TN = True Negative, FP = False Positive, and FN = False Negative.  
**Precision:** Precision is the total number of examples that were correctly identified as being in a certain class. Precision is defined as the ratio of correctly predicted cases to all expected cases. The formula below can be used to compute it.  
\[\text{Precision}=\left(\text{TP}/(\text{TP}+\text{FP})\right)^{*}100, \tag{2}\]  
where TP = True Positive and FP = False Positive.  
**Recall:** Recall is the ability of the classifier to correctly recognize each positive case. Recall is defined as the proportion of true positives to the total of both true positives and false negatives.  
\[\text{Recall}=\left(\text{TP}/(\text{TP}+\text{FN})\right)^{*}100, \tag{3}\]  
where TP = True Positive and FN = False Negative.  
**F1 Score:** The F1 score may be regarded as a harmonic mean of accuracy and recall, with the greatest value being 1 and the worst value being 0. Precision (P) and recall (R) both contribute equally to the F1 score.  
\[\text{F1 score}=2^{*}(\text{P}^{*}\text{R})/(\text{P}+\text{R}), \tag{4}\]  
where P = precision and R = recall.  
**Confusion Matrix**: A confusion matrix is a table that lists the effectiveness of a classification model. It provides details about the model's accuracy and flaws by showing the number of true positives, true negatives, false positives, and false negatives. The confusion matrix can be used to calculate many metrics, including accuracy, precision, recall, and F1 score, which aid in assessing the efficacy of the model.