esa-sceva/satcom-chunk-collection huggingface dataset:
# The Effect of Carob Extract on Antioxidant, Antimicrobial and Sensory Properties of Bread
## 3 Results and Discussion
### Antioxidant Potential of bread with the Addition of Carob Extract  
The antioxidant potential of bread with the addition of CP was defined based on the results of total phenol content (TP), as well as the in vitro antioxidant activity determined by DPPH, FRAP and ABTS tests (Table 2).  
It can be noted that the addition of CP caused the increment of TP content in the obtained bread samples. The highest TP content (0.27 mg GAE/g) was observed in sample E3.5%, and was two times higher in comparison to the control. The ability to neutralize DPPH radicals was significantly increased (_p_ < 0.05), even by the addition of the lowest amount of CP (0.5%), which was also the case when observing the reducing power towards Fe\({}^{3+}\) (FRAP). The antioxidant activity of bread determined by the ABTS assay was significantly higher (_p_ < 0.05) compared to the control sample when the amount of 1.5% of CP was applied. The highest values for in vitro antioxidant activity were achieved by the addition of 3.5% of CP: DPPH of 1.24 \(\upmu\)M TE/g (31 times higher), FRAP of 1.19 \(\upmu\)M Fe\({}^{2+}\)/g (1.7 times higher) and ABTS of 1.96 \(\upmu\)M TE/g (2.3 times higher compared to the control).  
Even though the application of carob flour extract in breadmaking has not yet been examined in the literature, the obtained results are in accordance with research publications that have investigated the addition of other types of plant extracts [46; 47]. On the basis of the obtained results, it was confirmed that with the addition of CP the bread had significantly higher bioactive compound content, i.e., higher antioxidant activity compared to white wheat bread, which contributes to its functionality.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Sample** & **TP (mg GAE/g)** & **DPPH (\(\upmu\)M TE/g)** & **FRAP (\(\upmu\)M Fe\({}^{2+}\)/g)** & **ABTS (\(\upmu\)M TE/g)** \\ \hline
**Control** & 0.12 \(\pm\) 0.04 a & 0.04 \(\pm\) 0.02 a & 0.70 \(\pm\) 0.03 a & 0.85 \(\pm\) 0.05 a \\
**E0.5\%** & 0.14 \(\pm\) 0.03 a & 0.12 \(\pm\) 0.02 b & 0.92 \(\pm\) 0.05 b & 0.89 \(\pm\) 0.03 a \\
**E1.5\%** & 0.18 \(\pm\) 0.03 a,b & 0.14 \(\pm\) 0.02 b & 0.99 \(\pm\) 0.03 b & 1.07 \(\pm\) 0.03 b \\
**E2.5\%** & 0.19 \(\pm\) 0.05 a,b & 0.76 \(\pm\) 0.12 c & 0.89 \(\pm\) 0.02 b & 1.92 \(\pm\) 0.02 c \\
**E3.5\%** & 0.27 \(\pm\) 0.04 b & 1.24 \(\pm\) 0.05 d & 1.19 \(\pm\) 0.02 c & 1.96 \(\pm\) 0.07 c \\ \hline \hline \end{tabular}  
* Results are expressed as mean \(\pm\) SD (n = 3); *d values in the same column for crust colour and for crumb colour mean that there are no significant differences between mean values at \(p\) < 0.05 (Duncan test).  
\end{table}
Table 2: Total phenolic content and antioxidant activity of bread with the addition of powdered carob extract.
# Estimation of Energy Storage Requirements in an Independent Power System from an Energy Perspective
## 2 Research Methodology  
### Electricity Consumption Data  
Taiwan's electricity load throughout the year is about 20-40 GW, while the lowest load occurs during the Spring Festival. In this period, the electricity load is the lowest when it is only about 16 GW in the early morning and about 19 GW at noon, as shown by the significant difference (dark blue area) at the bottom of Figure 1. As Taiwan is located in the Northern Hemisphere, solar power generation in winter will be slightly lower than in summer. Wind power generation depends on the monsoon, which occurs mainly in winter, as shown in Figure 2. As the Spring Festival has the lowest electricity load and abundant wind power generation, it is the annual period when energy storage is most needed in Taiwan.  
Power generation of renewable energy does not change, regardless of whether it is weekday or weekend. However, the power load in Taiwan is particularly low on Sundays, as shown in the dark blue area in Figure 1. Therefore, winter Sundays are the time when energy storage is the second most needed in Taiwan.  
Based on the estimation of the Ministry of Economic Affairs in Taiwan, the electricity load growth rate is predicted to be 2% from 2023 to 2030 [16]. According to the local Independent System Operator (ISO), power generation during the Spring Festival will mainly consist of gas units, wind power, and PV. The power generation schedule for the 2026 Spring Festival is simulated in Figure 3[17]. From this figure, the net load at noon is about 3 GW. From the same figure, we saw that the local ISO calculates the minimum power generation of the power system to be about 6 GW. So, to maintain system frequency and voltage, it is not possible to entirely disconnect or shut down every traditional unit,  
Figure 1: Heat map of Taiwan’s electricity load in 2022 (Data Source: Taiwan Power Company, Taipei, Taiwan).  
and a certain number of them must be retained. In this case, storage pumps pump water, and batteries are charged to store excess energy and avoid curtailment. To estimate the required energy storage capacity needed from 2025 to 2030 to avoid curtailment in scenarios with a large proportion of renewable energy, this study will use the actual electricity consumption data from the 2023 Spring Festival, along with the annual electricity growth rate of about 2%, to project the electricity consumption from 2025 to 2030.
# A Model and Quantitative Framework for Evaluating Iterative Steganography
## 1 Introduction
### Steganography
Higher robustness ensures that the embedded message is more likely to remain intact or that a larger portion of the original information is retained after such modifications [13]. A typical steganographic method is characterized by two core functions: an encoding function, which embeds the message within the cover medium, and a decoding function, which retrieves the embedded message from the steganogram:  
* Encoding function--maps a given cover object and a message to a steganogram. The encoding process embeds the message within the cover medium while minimizing perceptible alterations to the cover medium,
* Decoding function--extracts the embedded message from a steganogram. It is designed to accurately retrieve the message without requiring access to the original cover object. Notably, the decoding function can also be considered as an integral part of the steganalysis process, as it helps verify the presence and content of hidden information [14]. The performance of these functions is assessed based on their ability to preserve the cover medium's integrity, maximize the capacity for concealed information, and withstand detection by steganalysis techniques. Designing these functions often requires managing significant trade-offs between undetectability, capacity, and robustness, which are crucial for the effectiveness of steganographic methods.
## 2 Research Background
### Research Gap  
Despite these advancements, research focused on enhancing the management of contractual reporting requirements remains relatively unexplored and fragmented. Most of the studies in the area of construction reporting have focused on the development of systems that improve data acquisition, information flow, and communication. While other studies, such as those mentioned previously, have deployed NLP and AI to automate information retrieval and extraction from construction contracts, the text approaches used to develop these requirement extraction models are limited by a lack of full automation, low extraction accuracy, and the absence of a cost-time prediction component [4; 5; 13]. Indeed, a review of construction literature could not identify any established study capable of automatically extracting reporting requirement statements from hundreds of pages of contractual and project specification documents.
## 2 Theoretical Background of Convolutional Neural Networks
### Model Overview
A window functioning similarly to the kernel mentioned in the convolutional layer glides through the feature map and keeps the maximum value from each patch, whereas all other elements are discarded. Classification: After this pattern of alternating convolutional and pooling layers is concluded, the output feature map of the last set of layers is flattened into a 1D array vector, which serves as the input of a traditional Feed-Forward Neural Network (FFNN) that is used to execute the classifications. The last notable part of the information in the forward propagation journey is the loss function. The main objective of the optimization procedure is the minimization of the loss function. The Categorical Cross-Entropy (CCE) loss function is introduced to better conceptualize and comprehend how the proximity between predicted and real values is quantified. The selection of the aforementioned loss function was made due to the nature of our application, which is a multilabel classification problem. Assuming that the total number of classes is M and the observations contained in the dataset are N, then accept that the vector containing the target values from the dataset is denoted as \(\mathbf{y}=[y_{1},\ y_{2},\ldots,y_{n}]\). Plausibly, the predicted values of the network are the output values of the last layer. Assuming that the network has L layers, and following the notation previously used, we express the predicted values as \(\mathbf{a}^{[L]}=\left[a_{1}^{[L]},\ a_{2}^{[L]},\ \ldots,\ a_{n}^{[L]}\right]\). Accepting CCE as our loss function then gives:  
\[E=CCE\left(\mathbf{y},\mathbf{a}^{[L]}\right)=-\sum_{i=1}^{M}y_{i}\log\left(a_{i}^{[L]}\right) \tag{2}\]  
Training a network is a process in which the model is self-improving. After several epochs, the output predictions and ground-truth labels should converge, hence minimizing the loss function. After the conclusion of the forward pass, the backpropagation takes place, where through the calculation of the gradient of the loss between the output estimations of the model, the model updates its learnable parameters, namely weights and biases, attempting to minimize loss by implementing gradient descent, which is used to update the learnable parameters of the model to minimize the loss, hence achieving better accuracy. Mathematically, a gradient is a partial derivative; we calculate the partial derivative of the loss function concerning each of the learnable parameters, and then the respective learnable parameter is updated with an arbitrary step size dictated by the learning rate, which is a hyperparameter:  
\[p=p-a\,\frac{\partial L}{\partial p} \tag{3}\]  
where \(p\) represents any learnable parameter and \(\alpha\) denotes the learning rate of the optimizer of the model. In Appendix A, Table A1 presents a comprehensive overview of the operations and the learning procedure of a 1D-CNN, demonstrating both the forward and back-propagation components of the network.
## 4 Conclusions  
A study for the deterministic synthesis of LFoV planar arrays is presented. The procedure is implemented through the initial design of a FFoV case with the prescribed \(HPBW\) and \(SLL\), and a desired directivity index. The next step comprises a scaling transformation, which results in a new enlarged array characterized by the same \(SLL\), shrunk \(HPBW\), and increased directivity. Design examples of both non-uniformly/uniformly excited periodic/aperiodic arrays verify the applicability of the proposed method. The presented procedure forms a communicating path between LFoV and FFoV design methods. In this way, it provides the means to shape the far-field pattern, in a finite angular range near the main beam, either following classical array synthesis methods or employing proper sampling on planar aperture distributions.  
Furthermore, since the proposed synthesis is deterministic, it can provide benchmark solutions or, preferably, be integrated into various stochastic methods [28], to initiate optimized solutions to practical [29] design problems.  
Figure 7: The power pattern of the designed array for various \(\varphi\) cuts (red: without element pattern, green: with element pattern).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Acknowledgments:** The author acknowledges the helpful discussions with [PERSON].  
**Conflicts of Interest:** The author declares no conflicts of interest.
## 3 Research Methodology
### Evaluation Parameters for Methodology  
An all-inclusive set of metrics is used to evaluate the tested technique regarding its resistance and how well it works:  
* Accuracy: The accuracy is a key factor used to measure to what extent the model's predictions are correct, providing a strategy to analyze its overall performance.  
Precision: This statistic examines the precision of positive predictions, emphasizing the model's ability to reduce false positives. It is especially pertinent in situations when accuracy is a crucial factor. This parameter checks the precision of positive predictions in reducing false alerts; it is important especially when accuracy matters most.
* Recall: Recall reveals how many instances are correctly identified versus misclassified data points, taking into consideration sensitive issues so that the system responds to relevant cases as expected without classifying anything incorrectly, and without caring about sensitivity and specificity. In preventing further occurrences of error type II, this value is required.
* F1 Score: F1 score provides the mean of both accuracy and recall, offering a comprehensive evaluation of the FL models. This is obtained through taking both false positives and false negatives into consideration, hence giving a balanced view.
* Confusion Matrix: A visual portrayal emphasizes the allocation of true positive, true negative, inaccurate positive, and erroneous negative predictions, offering a full overview of the model's efficacy across multiple categories.  
These well-selected evaluation criteria, taken together, provide a detailed and complex picture of the performance of the approach in many aspects, guaranteeing a thorough evaluation of its effectiveness.
# An Evaluation of Machine Learning Models for Forecasting Short-Term U.S. Treasury Yields
## 5 Discussion
### Interpretation and Theoretical Implications  
The superior performance of nonlinear models such as random forest (RF) and multilayer perceptron (MLP) over linear regression (LR) highlights the limitations of linear modeling in capturing the complex dynamics between macroeconomic variables and bond yields. However, this performance gain comes with a notable trade-off in interpretability.  
While the RF and MLP models can uncover intricate nonlinear interactions and offer high predictive accuracy, they often function as \"black boxes,\" providing limited transparency into how specific inputs influence outputs. In contrast, LR offers full interpretability via model coefficients, which is particularly valuable in policymaking and regulatory environments, where understanding causality, accountability, and model rationale is essential.  
For financial analysts, traders, and policymakers, this trade-off means that model selection should be context dependent. In high-stakes environments, such as central banking decisions or fiscal policy evaluation, slightly lower predictive performance might be acceptable if the model provides interpretable insights. Conversely, in trading or algorithmic forecasting scenarios, where real-time accuracy is paramount, models like RF may be preferable despite reduced transparency.  
Future work should explore explainability techniques, such as SHAP (SHapley Additive Explanations) or LIME (Local Interpretable Model-agnostic Explanations), to bridge this gap by enhancing the interpretability of complex models. These tools can help decompose model predictions into intuitive variable contributions, making even black-box models more suitable for policy-related applications.
# Artificial Neural Network-Based Mechanism to Detect Security Threats in Wireless Sensor Networks
## 4 Proposed Methodology  
Our proposed ANN-based mechanism is composed of three layers. Because obtaining real datasets for WSNs is difficult, we used the CICIDS2017 dataset [34], provided by Canadian Institute of Cybersecurity, to train and test the proposed model. This dataset contains most of the currently relevant attack scenarios, which were enough to evaluate the effectiveness of our proposed system. The proposed model was trained with the four features extracted from CICIDS2017. These parameters are as follows: number of packets received and forwarded by the node, energy-consumption details of the node [14], and the trust value of the node [19]. We had four nodes in the input layer. During training, various numbers of nodes in the hidden layer and various number of hidden layers were evaluated; however, the false-detection rate was determined to be low when there was one hidden layer that contained four nodes. As we are interested in the detection of three specific routing attacks due to their high-severity impacts, four nodes were used to make up the output layer. This layer detects whether the node is normal or malicious; a malicious node is involved in launching black-hole, gray-hole, wormhole attacks using the following outputs: [1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], and [0, 0, 0, 1], respectively. The NS2 simulator was used to obtain the results, which consist of the detection of the intended routing attacks.  
Our ANN-based proposed technique operates in two parts. The first part involves training the system using the dataset that was obtained; the second part involves testing the trained system using actual traffic that was obtained via a network interface. When effectively trained with both normal and attack traffic, ANN-based algorithms yield optimal outcomes [35]. The dataset employed in our suggested mechanism has 20,000 input vectors, of which 15,000 are used for training, 3000 input vectors are used for testing, and the remaining 2000 input vectors are used to validate the proposed detection system.
# A Novel Adaptive Model Predictive Control Based Three-Phase Inverter Current Control Method
###### Abstract  
This paper proposes a novel current control method based on Model Predictive Control (MPC) for three-phase inverters. The proposed method is based on an Adaptive MPC (A-MPC) with a PWM modulation. An innovative model parameter estimation and modification method is also proposed, leading to enhanced control accuracy. Comparing with traditional current control methods, such as PI and PR control, the proposed method has better dynamic performance. The transient dynamics, i.e., recovery time and overshoot, have been considerably improved. Simulation and experimental results are presented to validate the effectiveness of the proposal.  
Inverters; model predictive control; current control; dynamic performance Article
## 3 Terpene Compounds  
The essential oils contributing to beer aroma profiles are secondary metabolites of the hop plant secreted in the lupulin glands. By definition, the hop oil fraction is the portion of the hop material that is volatile. These volatile aroma compounds are considered 'essential' since they give hops their characteristic smell, especially for terpene compounds, including monoterenes and sesquiterpenenes.  
Terpene biosynthesis in plants involves two pathways: the plastitial methylerythnitol phosphate (MEP) pathway and the cytosolic mevalonate (MVA) pathway, both of which yield the general 5-carbon isoprenoid diphosphate precursors of all terpen. These pathways ultimately control the different substrate pools available for terpene synthases (TPS). As the research previously reported [61], 1015 proteins were identified by an extensive 2D-LC-MS/MS analysis in hops. The MEP pathway enzymes, which are indeed the upstream part of the terpenoid biosynthetic pathway, were all identified while only one enzyme of its cytosolic counterpart in the MVA pathway was ascertained here. It suggests a predominant role for the MEP pathway in terpenoid synthesis occurring in hop glandular trichomes.  
For the MEP pathway, the major genes responsible for terpene compounds production have been reported previously [62] and we will not address them further here. Upstream enzymes were thought to be critical for flux regulation including: 1-deoxyxylulose-5-phosphate synthase (DXS), 1-deoxy-d-xylulose-5-phosphate reductoisomerase (DXR), 4-hydroxy-3-methylbut-2-enyl diphosphate synthase (HDS), and 4-hydroxy-3-methylbut-2-enyl diphosphate reductase (HDR) [62; 63]. However, the reaction catalyzed by these enzymes is considered a rate-limiting step depending on the species, tissue, and developmental stage [64]. Additionally, monoterpene synthesis in some plants relies on both geranyl diphosphate synthase (GPPS) and monoterpene synthase (MTS) activities [65]. In hops, the MTS enzymes and the linalool synthases (LIS) were discussed more in recent re search, which were responsible for the biosynthesis of \(\beta\)-myrrene and linalool, respectively. Therefore, we focused on the studies reviewing the mentioned enzymes in hops.
# Mycelium-Based Thermal Insulation for Domestic Cooling Footprint Reduction: A Review
## 8 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Material**} & \multirow{2}{*}{\begin{tabular}{c} **Specific Heat** \\ **(kJ/kg-K)** \\ \end{tabular} } & \multirow{2}{*}{**Density kg m\({}^{-3}\)**} & \multirow{2}{*}{\begin{tabular}{c} **Thermal** \\ **Conductivity** \\ **(W m\({}^{-1}\)K\({}^{-1}\))** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Cost** \\ **(\(\overline{\ell}\)/m\({}^{2}\))** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Durability** \\ **(years)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Water Absorption** \\ **Rate (\%)** \\ \end{tabular} } & \multirow{2}{*}{**Ref.**} \\ \hline Glass Wool & 0.85 & 12–64 & 0.03–0.45 & 77.5–147 & 20–30 & 75 & [40; 95; 96; 97; 98; 99] \\ \hline Rock Wool & 0.7 & 20–64 & 0.03–0.040 & 102.08–179.5 & 30–50 & N/A & [40; 100; 101; 102; 103] \\ \hline Foam Glass & N/A & N/A & 0.038–0.055 & 357.38–445.5 & 50–80 & N/A & [40; 104] \\ \hline Perlite & 0.9–1.0 & 30–400 & 0.040–0.055 & 207.89 & 50 & N/A & [40; 97; 105; 106] \\ \hline Vermiculite & 0.8–1.0 & 170 & 0.062–0.090 & 152.6 & N/A & N/A & [40; 97; 107] \\ \hline Aerogel & 1.05 & 150–220 & 0.015–0.028 & 168.04 & 10 & 50 & [97; 108; 109; 110] \\ \hline EPS & 1.25 & 15–30 & 0.03–0.040 & 61.42–186.56 & 50 & 25 & [40; 97; 111; 112; 113] \\ \hline XPS & 1.3–1.7 & 24–45 & 0.025–0.040 & 156–180 & 50 & N/A & [40; 97; 114] \\ \hline Polyurethane & 1.3–1.45 & 31.5–35 & 0.021–0.040 & 303.78 & 30–50 & 30 & [40; 97; 115; 116; 117; 118; 119] \\ \hline Cellulose & 1.3–2.0 & 30–80 & 0.035–0.042 & 175.71 & 20–30 & 60 & [117; 118; 119; 120; 121] \\ \hline Cork & 1.5–1.7 & 80–115 & 0.035–0.050 & N/A & 50 & N/A & [64; 97] \\ \hline Sheep wool & 1.9–2.0 & 30 & 0.033–0.040 & 33.9 & 60–100 & 35 & [40; 97; 120; 121] \\ \hline Flax & 1.4–1.6 & 30–40 & 0.038–0.042 & 116.77 & N/A & 12.3 & [40; 97; 122] \\ \hline Hemp & 1.6–1.7 & 38–41 & 0.039–0.06 & 108.1–138.93 & N/A & 20 & [40; 97; 123] \\ \hline Mycelium & 7.4–10.2 & 51.098–1420 & 0.029–0.081 & 15.62–34.32 & 20 & 17.2–37.6 & [21; 26; 27; 124; 125] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Thermo-physical, Cost, Water Absorption, and Durability Properties.