esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### E-Nose  
In the present study, the aromatic feature of these wine samples was analyzed under 10 chemical sensors under the E-nose analysis, and their aromatic differences were analyzed using principal component analysis (Figure 1) [23; 24]. The first and second principal component represented 95.8% and 2.6% of the total variance, respectively. An obvious segregation among these passion fruit wine samples was observed in the scores plot (Figure 1A). For instance, the CY and ES-strain-fermented passion fruit wines were positioned at the positive side of the PC1. However, the position of these two strain-fermented wines at the PC2 separated these two wine samples from each other (the CY wine at the negative side of the PC2 and the ES wine at the positive side of the PC2). Similarly, the BV-strain-fermented wine was situated at the positive side of the PC2 and near the zero point of the PC1. The VI-fermented passion fruit wine resided at the left corner of the score plot (the negative side of both PC1 and PC2). In the loading plot (Figure 1B), the MOS8 (broad-alcohol) and MOS6 (broad-methane) signals exhibited a primary contribution on the PC1. Meanwhile, the signals derived from the MOS2 (broadrange), MOS7 (sulfur-organic), MOS9 (sulph-chlor) and MOS1 (aromatic) also contributed their effect to the PC1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameters** & **Yeast Strain** & & & \\ \hline  & ES & **BV** & **VI** & **CY** \\ \hline Residual sugar (g/L) & 3.43 \(\pm\) 0.47 ab & 5.2 \(\pm\) 0.98 a & 4.31 \(\pm\) 0.49 a & 1.57 \(\pm\) 0.04 b \\ Total titutable acidity (g/L) & 9.84 \(\pm\) 0.50 a & 8.54 \(\pm\) 0.42 a & 9.69 \(\pm\) 0.05 a & 9.48 \(\pm\) 0.33 a \\ Volatile acidity (g/L) & 0.10 \(\pm\) 0.01 a & 0.08 \(\pm\) 0.01 a & 0.13 \(\pm\) 0.09 a & 0.08 \(\pm\) 0.01 a \\ Total sulfur (mg/L) & 199.6 \(\pm\) 79.76 a & 82.4 \(\pm\) 10.18 a & 204.0 \(\pm\) 112.01 a & 211.6 \(\pm\) 3.96 a \\ Alcohol content (\% v/v) & 9.35 \(\pm\) 0.74 a & 9.63 \(\pm\) 0.45 a & 9.15 \(\pm\) 0.66 a & 9.31 \(\pm\) 0.39 a \\ pH & 3.15 \(\pm\) 0.04 a & 3.34 \(\pm\) 0.11 a & 3.19 \(\pm\) 0.03 a & 3.23 \(\pm\) 0.04 a \\ Total flavonoids (mg Rutin/L) & 179.81 \(\pm\) 9.69 ab & 148.29 \(\pm\) 4.26 b & 189.96 \(\pm\) 6.2 ab & 213.26 \(\pm\) 24.03 a \\ \hline \hline \end{tabular}  
* Data are the mean \(\pm\) standard deviation of duplicate tests. Different letters in each row indicate significant difference at a significant level of 0.05.  
\end{table}
Table 1: Physicochemical indices and total flavonoids content in passion fruit wine fermented by commercial yeast strain ES488 (ES), BV818 (BV), VIC (VI) and CY3079 (CY).
## 4 MADDPG-Based Integrated Space-Air-Sea Network System Cost Minimization Algorithm
### Partially Observable Markov Decision Process (POMDP) Strategy
#### 4.1.3 Reward Function  
Generally, the reward in a real-time network is related to the objective function. In the optimization problem considered, the optimization goal is to minimize the total system cost, which is the weighted sum of the delay and energy consumption. To prevent the intelligent agents' decisions from violating the computational resource limitations and UAV collision constraints, penalties are applied to agents that violate the constraints. Specifically, in multi-agent reinforcement learning, multiple agents collaborate to achieve the goal of minimizing the system cost. Therefore, all the agents share a common reward function, which is defined as \[r_{i}(t)=-\text{Cost}_{i}(t)-\eta_{i}\text{Pen}_{i} \tag{18}\]  
Here, \(\eta_{i}\) represents the binary penalty factor indicating whether an intelligent agent violates a constraint. If the constraint is violated, a penalty will be applied.
# Improving Prostate Image Segmentation Based on Equilibrium Optimizer and Cross-Entropy
## 4 Minimum Cross-Entropy by EO for MRIs
### Problem Formulation  
The proposed method adopts a multilevel thresholding approach for image segmentation, where a prostate image is segmented based on pixel intensity, dividing the histogram into a finite number of classes. This partitioning is achieved by selecting a set of threshold values across the histogram. The equilibrium optimizer (EO) is employed to generate candidate segmentation configurations, while the minimum cross-entropy (MCE) criterion evaluates the effectiveness of these configurations. Through an iterative process, EO and MCE collaborate to identify optimal threshold values that yield effective image segmentation results.  
As outlined in Section 2, the minimum cross-entropy thresholding can be formulated as an optimization problem, which is expressed as:  
\[\begin{array}{ll}argmin_{th}&f_{Cross}(\mathbf{th})\\ \text{subjectto}&\mathbf{th}\in\mathbf{X}\end{array} \tag{19}\]  
This corresponds to the MCE formulation from Equation (6). The constraints for the feasible solution space are defined by the possible pixel intensity values in an 8-bit representation, where the pixel values range from 0 to 255. The set of feasible thresholds is given by: \(X=\left\{th\in R^{n}|0\leq th_{j}\leq 255,j=1,2,\ldots,n\right\}\).
## 2 Design
### Hardware  
For the proposed system, commercial Tuya Smart devices, Arduino sense modules, and an ESP32 microcontroller are used to turn on and off the motor, measure the current in each power supply line, and measure the temperature near the motor area and the vibration level of the engine. Tuya Smart devices are chosen since this manufacturer offers a cloud platform for developing IoT projects, and its devices are Alexa-compatible.  
In the case of power on, power off, and rotation change of the motor, smart WiFi relays compatible with Amazon Alexa are used. These devices are characterized by their use in loads up to 10 A. They activate electromechanical relays, which activate three-phase contacts to turn on, turn off, or perform motor turn changes. Mechanical relays protect intelligent devices, meaning that the WiFi relays do not activate the contactors directly. Figure 1 shows the components discussed above.  
To measure the motor housing temperature, a temperature and humidity sensor developed by Tuya Smart and compatible with Amazon Alexa was used, which can be configured and reviewed with the Tuya Smart mobile application and the IoT platform in the cloud. It should be noted that the above devices only work with a connection to a 2.4 GHz WiFi network.  
The ACS712 ELCTR-05B-T module (hall effect current sensor) is used with the ESP32 microcontroller board to measure the current value in each motor phase. This module sends the measured values to the Blynk platform so that they can be accessed with the  
Figure 1: Control and power circuits of motor drive prototype with [PERSON].  
VoiceFlow platform, where the skill of this system was programmed. Due to the proposed application, the ACS712 ELCTR-05B-T model has been selected, which measures up to \(\pm\)5 A current. Regarding vibrations, the ADXL345 module analyzed the movements in three axes. When exceeding a specific user tolerance, the module will show that the motor operates under abnormal conditions, as the current module works with the ESP32 board. Figure 2 shows the PCB board developed for the existing vibration modules and the ESP32. Figure 2 also shows the temperature module located above the motor.
## 3 Results and Discussion
### Application of the Modified Methodology
A repeated evaluation of the model and trajectory results revealed that for these 15 days the DD identification could easily be biased by subjectivity since results from the models and trajectories do not show a clear picture. An additional four days show an NDL\({}_{\text{MB}}\) below 4 ug/m\({}^{3}\) while PM\({}_{10}\) concentrations at DB range from 56 to 92 ug/m\({}^{3}\)--again pointing to both statements. These days could be clearly identified as DD days and thus point to a limitation of the threshold method. Thus, the evaluation of single events remains of great importance, but should be supported by a chemical analysis of the crustal loads as given below exemplarily for two cases.
# Application of SSIR Method for the Design of Fungicides
## 1 Introduction
The encodings used by SSIR are listed below in Table 1: the second column stands for the molecular encoding and the last three columns specify the condition of being active (compound of interest or positive) or non-active (negative or non-interesting compound) as a fungicide for three fungal species, namely, cucumber downy mildew (CDM), rice blast (RB) and cucumber gray mold (CGM). The residue codes obey to the following equivalences (some are not present in the [PERSON] et al. article):  
* A: 'H' at all sites;
* B: '-CH3' at R1 site and '-Cl' at sites R2-R6;
* C: '-F' at R2, R4 and R6 (not present at R6 in Table 1 of [PERSON] et al. );
* D: '-NO2' at R2 and R6, '-C(CH3)3' at R4;
* e: '-CH3' at site R2 (this is not mentioned in [PERSON] et al., but in a personal communication of these authors);
* E: '-CN' at R4;
* F: '-NO2' at R4;
* g: '-CH3' at R4;
* h: '-COCH3' at R4;
* i: '-OCH3' at R4. \begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Compound**} & **SSIR** & **Of Interest** & **Of Interest** & **Of Interest** \\  & **Encoding** & **for CDM 1** & **for RB 2** & **for CGM 3** \\ \hline P1 & AAAAAA & (N) & (N) & N \\ P2 & ABAAAA & N & N & N \\ P3 & AABAAA & N & N & N \\ P4 & AAABAA & N & N & N \\ P5 & AAAgAAA & N & Y & N \\ P6 & AAAhAA & N & N & N \\ P7 & AAAEAA & (N) & N & N \\ P8 & AAAFA & N & N & N \\ P9 & AAAiAA & N & N & N \\ P10 & AAADA & N & N & N \\ P11 & ABBAAA & N & N & N \\ P12 & ABABAA & N & N & N \\ P13 & ABAAAB & N & N & N \\ P14 & AABBAA & N & (N) & N \\ P15 & AABABA & N & (n) & Y \\ \hline \end{tabular}
\end{table} 
Table 1: Molecular encodings with the qualitative activity as fungicide against three species. The respective percentages of susceptibility have been collected for several application doses of the tested molecules. These percentages are continuous variables, but in both procedures (linear classifier or SSIR) cutoff levels must be defined to classify the training compounds as active or not. Table 1 lists the groups defined in this study. For CDM a minimum level of 90% of susceptibility was required, which defines a total of 9 molecules of interest or active (25.5% of 36 with known value). For RB, a minimum level of 100% was required, and 15 molecules are of interest (48.4% of 31). For CGM, a minimum level of 80% was considered, and 8 molecules were defined a priori as being of interest (20% of 40). The compound fluazinam was considered in the paper by [PERSON] et al. but it is not considered in the present study because this structure, despite being similar to the other molecules, cannot be encoded using the same scaffold. SSIR deals with a single scaffold, in the form applied here.
# Limitations and Performance Analysis of Spherical Sector Harmonics for Sound Field Processing
## 7 Conclusions  
In this paper, we revealed three main limitations of spherical sector harmonics by investigating their performance under different scenarios. We first proved that spherical sector harmonics are not solutions to the Helmholtz equation. Then, due to the violation of the Helmholtz equation, spherical sector harmonics lack the ability to conduct sound field radial extrapolation. The simulation results revealed that the accurate estimation of the extrapolated sound field using spherical sector harmonics is challenging, with reasonably accurate estimates possible only with low frequencies, large sector sizes, and small extrapolation distances within the exterior case. Thirdly, a distortion issue inherent to spherical sector harmonics was identified. Due to the mapping process in spherical sector harmonics, severe distortion exists around the edge of the reconstruction sector region. Based on the three limitations, the future implementation of spherical sector harmonics should be focused on the processing and analysis of the measurement sector region without any radial extrapolation process, and the measurement region should be larger than the target sector region.  
Figure 12: Reconstruction error in the azimuth direction \(\epsilon_{a}(\phi,k)\) with different \(\phi\) values.  
**Author Contributions:** Conceptualization, H.B., F.M., T.D.A. and P.N.S.; funding acquisition, T.D.A. and P.N.S.; investigation, H.B. and S.X.; methodology, H.B.; project administration, T.D.A.; supervision, T.D.A., F.M. and P.N.S.; validation, H.B. and S.X.; writing--original draft, H.B.; writing--review and editing, T.D.A., F.M., S.X. and P.N.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work is sponsored by the Australian Research Council (ARC) Discovery Projects funding schemes with project numbers DP200100693.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Publicly available datasets were analyzed in this study. This data can be found here: [[https://github.com/[PERSON]/Limitations-of-SSH.git](https://github.com/[PERSON]/Limitations-of-SSH.git)]([https://github.com/[PERSON]/Limitations-of-SSH.git](https://github.com/[PERSON]/Limitations-of-SSH.git)).  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# A Survey of Wi-Fi 6: Technologies, Advances, and Challenges
## 8 Modulation Techniques
### Forward Error Correction (FEC)  
Forward Error Correction (FEC) helps a receiver to detect and possibly correct transmission errors. The source inserts redundancy in its transmission so that the receiver can detect the erroneous symbols and ideally correct them. More precisely, convolution codes encode \(m\) bits of information into an \(n\) bit symbol. Obviously, \(n\) is larger than \(m\), and a larger difference increases the robustness. Each MAC protocol data unit is processedindependently and a transmission failure means that the corresponding frame has to be retransmitted entirely.  
Binary Convolution Codes (BCC) and Low-Density Parity Check (LDPC) are two FECs used to reduce the probability of data loss in noisy channels. BCCs support up to a 242-tone RU. Alternatively, LDPC is applicable in Wi-Fi 6 for MCS 10-11 and/or RU sizes larger than 242-tone [175; 176]. In other words, a BCC is mandatory for bandwidths smaller than or equal to 20 MHz; otherwise, LDPC is used for larger bandwidths. Indeed, a BCC is inefficient for large bandwidths, requiring more time to process data and more encoders [177].
## 4 Results
### Optimal Configurations for Different Models Across Different Performance Metrics
#### 4.2.7 Optimal Configurations for Area Under the ROC Curve (AUC)  
Table 9 presents the configurations maximizing AUC. ResNet50 achieves the highest AUC of 0.996138 with SGD (learning rate 0.01), CosineAnnealingWarmRestarts, and CrossEntropy, where cyclical learning enhances class separation. ResNet101 follows at 0.995715 with SGD (learning rate 0.01), StepLR, and CrossEntropy, ensuring stable optimization. EfficientNet-B2 (0.995261) uses AdamW (learning rate 0.0001), StepLR, and LabelSmoothing, benefiting from adaptive regularization.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Optimizer** & **Learning Rate** & **Scheduler** & **Loss Function** & **IOU** \\ \hline ResNet50 & AdamW & 0.0001 & CosineAnnealingLR & FocalLoss & 0.926174 \\ ResNet101 & SGD & 0.01 & StepLR & CrossEntropy & 0.917219 \\ EfficientNet-B2 & SGD & 0.01 & StepLR & CrossEntropy & 0.911475 \\ DenseNet121 & SGD & 0.01 & ReducedLROnPlateau & CrossEntropy & 0.90301 \\ MobileNetV3 & AdamW & 0.0001 & ReducedLROnPlateau & CrossEntropy & 0.90099 \\ ConvNeXt & Adam & 0.0001 & CosineAnnealingWarmRestarts & CrossEntropy & 0.898026 \\ ViT-B-16 & SGD & 0.001 & ReducedLROnPlateau & CrossEntropy & 0.878689 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Optimal hyperparameter configurations for maximizing intersection over union (IoU) across deep learning models.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Optimizer** & **Learning Rate** & **Scheduler** & **Loss Function** & **mAP** \\ \hline ResNet101 & SGD & 0.01 & StepLR & CrossEntropy & 0.992104 \\ ResNet50 & SGD & 0.01 & CosineAnnealingWarmRestarts & CrossEntropy & 0.991379 \\ DenseNet121 & SGD & 0.01 & CosineAnnealingWarmRestarts & FocalLoss & 0.989826 \\ EfficientNet-B2 & AdamW & 0.0001 & StepLR & LabelSmoothing & 0.989524 \\ ConvNeXt & SGD & 0.001 & CosineAnnealingLR & LabelSmoothing & 0.986675 \\ ViT-B-16 & SGD & 0.001 & ReducedLROnPlateau & LabelSmoothing & 0.985686 \\ MobileNetV3 & SGD & 0.01 & StepLR & CrossEntropy & 0.98276 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Optimal hyperparameter configurations for maximizing mean average precision (mAP) across deep learning models.  
DenseNet121 (0.995232) employs SGD (learning rate 0.01), CosineAnnealingLR, and CrossEntropy, with smooth learning rate decay aiding discrimination. ConvNeXt (0.99426) and ViT-B-16 (0.993844) use SGD (learning rate 0.001) with CosineAnnealingLR and StepLR, respectively, and LabelSmoothing or CrossEntropy, while MobileNetV3 (0.993534) mirrors ResNet101's SGD setup.
## 3 Simulation Results of the Elastoplastic SW-DVIB Method
### Plastic Deformation Simulation in a Cracked Plate  
The simulated case involved the uniaxial tension of a rectangular plate featuring a pre-crack. As illustrated in Figure 5a, the sample used in the simulation measured \(10\) cm \(\times\)\(10\) cm with a centrally located pre-crack extending \(5\) cm in length. The three-node triangular cell configuration comprising a total of 25,654 triangular cells is applied. The displacement loading path, depicted in Figure 5b, consists of a loading phase during the first \(10\) seconds, followed by an unloading phase from the \(10\) to the \(20\)th second. The micro-bond parameters used as inputs were \(E=40\) GPa, \(\bar{\varepsilon}_{y}=1\times 10^{-3},\alpha=0.2,\rho=2400\) kg/m\({}^{3}\).  
The simulated stress-strain relationship is shown in Figure 6a. There is an obvious linear segment from point A to point B and a nonlinear segment from point B to point C in  
Figure 5: Loading process of the case of the rectangular plate with a pre-crack: (**a**) model diagram; (**b**) displacement loading path.  
the loading phase. The unloading curve is parallel to the initial loading curve. This indicates that Young's modulus in the unloading stage is corroborated by the initial Young's modulus. When the axial stress decreased to 0 MPa, i.e., point E in Figure 6a, the specimen exhibited an obvious unrecoverable plastic deformation. The stress in the specimen changed from tensile to compressive as the strain decreased from Point E to Point F.  
The crack-opening deformation is shown in Figure 6b(B-D), corresponding to points A-F, respectively, as shown in Figure 6a. In the loading stage, the crack opening deformation increased with an increase in displacement. When the axial stress dropped to 0 MPa, the crack did not close, as shown in Figure 6b(F), which was caused by unrecoverable plastic deformation. These simulated phenomena demonstrate that the model can be used to model irreversible material deformation.
## 5 Experiments and Assumptions on Abnormal Behavior Detection and Noise Removal Performance
### Parameters Used in Ensemble Model for Attack Detection and Noise Reduction
#### 5.3.1 Decision Tree  
The decision tree model uses the criterion for measuring the impurity of each classification group and the split criterion in the process of splitting for classification. To construct a model that can be comprehensively applied to datasets of various distributions, we used the Gini index, which is less affected by the influence of data distribution. In addition, since the learning time of the decision, is relatively short, we choose the 'best' split method to derive high accuracy even though this approach takes some time.
# Fragility Assessment and Reinforcement Strategies for Transmission Towers Under Extreme Wind Loads
## 1 Introduction
Their study introduced a multi-stage damage detection framework based on multi-scale finite element modeling, enhancing accuracy in identifying failure progression. [PERSON] et al. [35] applied multi-scale techniques to analyze the failure modes of critical tower nodes under extreme weather conditions, revealing detailed local damage patterns that would be overlooked in single-scale models. [PERSON] et al. [36] conducted a comparative study of hierarchical and concurrent multi-scale modeling approaches, demonstrating that the concurrent method offered superior accuracy in capturing structural responses and stress distributions. While numerous studies have addressed transmission tower failure mechanisms, angle steel reinforcement techniques, and multi-scale simulation methods, few have provided a systematic and quantitative evaluation that directly links failure mechanisms to the effectiveness of reinforcement strategies. To bridge this gap, the present study aims to enhance the load-bearing capacity of transmission towers by conducting a comprehensive structural analysis of a representative 220 kV tower using the finite element software Abaqus [37]. The research investigates the tower's behavior under various wind attack angles, with a particular focus on identifying instability patterns and collapse mechanisms induced by extreme wind loading. Critical structural vulnerabilities that contribute to failure are systematically examined. To mitigate these weaknesses, a concurrent multi-scale reinforcement model is developed, allowing for detailed simulation of local reinforcement effects within the context of global structural behavior. The model is validated through numerical simulations to ensure its reliability.
## 2 Materials and Methods
### Mapping Aboveground Carbon Content in Saltmarsh Using ArborCam  
Based on the ArborCam normalized band difference analysis, and given that most quadrats did not have _J. krawsii_ present (suggesting that it is not dominant across the study site), simple linear regression between the normalized difference of ArborCam bands 4 and 7 (i.e., the standard NDVI) and the aboveground carbon content for the herbs, grasses, and sedges quadrats was used to calibrate the ArborCam image to aboveground carbon content. Non-saltmarsh areas were masked out from the final aboveground carbon content map using the height stratified vegetation cover data supplied by ArborCarbon. This allowed for a qualitative analysis of the fine-scale, spatial variation in the aboveground carbon across the study area.  
Due to limited access to the field site due to COVID-19-related travel restrictions, further field sampling could not be carried out to validate the map of aboveground carbon. Therefore, leave-one-out cross-validation (LOOCV) was used to validate the linear regression model to predict aboveground carbon using the NDVI. Using the LOOCV method, one quadrat is removed at a time, and the remaining dataset is used to create a model to predict the value of the excluded quadrat. This process is repeated for each quadrat, and the mean absolute error (MAE) and root mean square error (RMSE) were calculated between the observed and predicted values from all model iterations [41].  
The aboveground carbon map was also used to calculate an estimate of mean aboveground carbon content. The aboveground carbon content map was resampled to 1 m, and the mean aboveground carbon content was calculated as the sum of pixel values within the study areas divided by the saltmarsh area.  
We also modelled the relationships between the ArborCam NDVI and the aboveground carbon for all 47 quadrats, and between the NDVI and the six outlier (_J. krawsii_) quadrats to understand the impact that the inclusion of the outlier quadrats would have had on the goodness-of-fit of the model used to map aboveground carbon.