esa-sceva/satcom-chunk-collection huggingface dataset:
# Coverage Optimization of WSNs Based on Enhanced Multi-Objective Salp Swarm Algorithm
## 3 Multi-Objective Optimization Deployment Model
### Determining the Multi-Objective Deployment Model
#### 3.2.3 Energy Consumption Model  
Sensor nodes are typically powered by batteries and cannot replenish energy after deployment in the monitoring area. Therefore, energy efficiency is an important evaluation metric in the field of coverage technology for WSNs.  
**Definition 1**.: _Energy of Zone \(E_{k}\)_  
\(E_{k}\) _is obtained by dividing the monitoring area into \(K\) equal grids, where \(k\) within the range \([1,M]\). The energy of the \(k\)-th grid is defined as the ratio of the cumulative remaining energy of all nodes within this grid to the total number of nodes in this grid, given by_  
\[E_{k}=\frac{\sum_{i=1}^{m_{k}}E_{ki}}{m_{k}} \tag{7}\]  
_where \(m_{k}\) represents the number of nodes in the \(k\)-th grid, and \(E_{ki}\) denotes the remaining energy of node \(i\) in the \(k\)-th grid._  
**Definition 2**.: _Energy of Zone \(E_{a}\)_\(E_{a}\) is defined as the difference between the maximum and minimum values of the grid energy divided by the maximum grid energy, representing the current energy balance level of the network in_  
\[E_{a}=\frac{\max(E_{k})-\min(E_{k})}{\max(E_{k})} \tag{8}\]  
_where \(E_{a}\) reflects the overall balance of energy consumption in the network, where a larger value indicates a more uneven energy consumption. Therefore, a smaller \(E_{a}\) corresponds to better network performance._
# On-Demand Scheduling of Command and Responses for Low-Power Multihop Wireless Networks +
## 4 SCoRe Design
### Faster \(N_{\text{dslot}}\) Updates  
Routing topology in wireless networks may change due to various reasons such as link fluctuations or node join/leave, and any inconsistency between route and schedule may result in significant performance loss. This problem will persist until SCoRe root receives updated and accurate \(N_{\text{dslot}}\) information, and since SCoRe rely on route control packets to propagate \(N_{\text{dslot}}\) information, its performance will depends on routing protocol's behavior. Most routing protocols, however, try to send routing updates as few as possible to improve energy efficiency and reduce control packet overheads (e.g., Trickle timer for RPL's DIO and DAO). Therefore, SCoRe requires other ways to respond to network changes as fast as possible. To detect and resolve this \(N_{\text{dslot}}\) inconsistency problem promptly, SCoRe employs two small but effective recovery techniques as shown in Figure 5.  
**Response time update.** Because SCoRe's timeslot scheduling is up to each parent and all response messages must pass through the parent of a sender, this is a great opportunity to resolve \(N_{\text{dslot}}\) inconsistency. An SCoRe node piggybacks its \(N_{\text{dslot}}\) in the response messages so that its parent can check/update its \(N_{\text{dslot}}\). When the parent forwards the message to its parent, it modifies the value of \(N_{\text{dslot}}\) field to its own demand. Through this recursive process, newly updated \(N_{\text{dslot}}\) information from a response source is aggregated and reaches the root at response time.  
**Dissemination time update.** SCoRe's command messages are based on link broadcast, and a parent also belongs to a child's 1-hop neighbors. Thus, a parent is also able to hear the command message transmission from its child, although it is meant to go downwards. SCoRe uses this characteristic to update any \(N_{\text{dslot}}\) inconsistency. An SCoRe node embeds its \(N_{\text{dslot}}\) into command messages also, and its parent can overhear and update \(N_{\text{dslot}}\) of that node.  
The overhead for these recovery schemes is just the size of \(N_{\text{dslot}}\) field, which is only 2 bytes in our implementation.  
Figure 4: An example of parallel transmission scenario.
## 5 Natural Compounds Modulating Key Enzymes in Aerobic Glycolysis  
Glucose transported to the cytoplasm can be phosphorylated by hexokinase (HK) with ATP as a phospho donor. Among the four isoforms of HK, HKII is frequently highly expressed in malignant cells and plays an important role in tumor initiation and progression [97]. [PERSON] et al. [98] extracted a natural steroid from _Ganoderma sinensis_ (NSGS) and identified it as the first natural inhibitor of HKII. In their study, a natural product, (22E,24R)-6\(\beta\)-methoxygosta-7,9(11),22-triene-3\(\beta\),5\(\alpha\)-diol, exhibited high binding affinity to HKII in vitro. Consistently, this compound showed clear inhibitory effects on human pancreatic cancer cells with 4-fold selectivity versus normal cells, suggesting its potential as a candidate drug in the therapies of pancreatic cancer [98].  
Pyruvate kinase (PK) catalyzes the final step of glycolysis by converting phosphoenolpyruvate and ADP to pyruvate and ATP [99]. The pyruvate kinase M2 (PKM2) is the major isoform of PK and overexpressed in many types of cancer cells [100]. Pachymic acid, a lanostane-type triterpenoid from Poria cocos, could bind PKM2 in the pocket of its natural activator, fructose-1,6-bisphosphate and block the glycolysis of breast cancer cells. Additionally, pachymic acid is also an inhibitor of HKII [41].  
The 6-phosphofructo-1-kinase (PFK) is a rate-limiting enzyme of glycolysis, and its inhibition can lead to breast cancer cell death [101]. Thus, PFK is a potential target in developing novel anticancer therapeutics. [PERSON] et al. [49] first demonstrated that resveratrol could directly inhibit PFK activity in both breast cancer cells and an in vitro assay by promoting its dissociation from fully active tetramers to low active dimers. Similarly, a study by [PERSON] et al. demonstrated that EGCG could also attenuate PFK activity through modulating its oligomeric structure [33].  
Phosphoglycerate mutase 1 (PGAM1) is a mutase catalyzing the reversible conversion of 3-phosphoglycerate to 2-phosphoglycerate in the glycolytic pathway and thus coordinates glycolysis and biosynthesis to support tumor growth [102]. EGCG was identified as a PGAM1 inhibitor with remarkably stronger potency than previously reported inhibitors, such as PGMI-004A. A mechanistic study showed that the inhibition of PGAM1 by EGCG was caused by conformational change upon their binding rather than competitive inhibition of the substrate. Through inhibiting PGAM1, EGCG decreased 2-phosphoglycerate production and further inhibited glycolysis and PPP, leading to reduced cancer cell proliferation [34].
## 6 Performance Evaluation
### Comparison of Security and Functionality Features  
Table 6 provides a comparative overview of the security and functionality features of the proposed Bio-2 FA-IoD protocol against selected schemes, based on the analysis in Section 5 and common features discussed in the IoD literature [1; 2; 5; 6; 30]. The proposed Bio-2 FA-IoD effectively provides strong mutual authentication and resists keylogging and shoulder-surfing attacks by adapting Khedr's logic [8] with biometrics. Its lightweight nature is a key advantage for the IoD. While user anonymity and perfect forward secrecy are areas for potential future enhancement (currently marked \"Partial\" and \"Limited\"), its resilience to drone capture is good due to secrets being primarily on the OD. The provision for dual formal verification (BAN logic and BPR model) adds confidence in its security design. Compared to protocols like HCALA [2] and HOOPOE [6] that offer broader features like blockchain integration or handover at higher complexity, Bio-2 FA-IoD focuses on a streamlined, secure, and efficient initial operator-to-GCS authentication suitable for many IoD scenarios.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Feature** & **Khedr [8]** & **Nyangaresi [5]** & **Jan [1]** & **HCALA [2]** & **HOOPOE [6]** & \
\begin{tabular}{c} **Bio-2 FA-IoD** \\ **(Proposed)** \\ \end{tabular} \\ \hline Mutual & Yes & Yes & Yes & Yes & Yes & **Yes** \\ Authentication & Yes & Yes (Implied) & N/A & N/A & N/A & Yes \\ Keylogging & Yes & Yes (Implied) & N/A & Yes (PIN on & N/A & Yes \\ Resist. & Yes & Yes (Biometric) & N/A &
\begin{tabular}{c} Yes (PIN on \\ User) \\ \end{tabular} & N/A & Yes \\ Anonymity & No & Yes & Partial & Yes & Yes & **Partial** \\ Replay Attack & Yes & Yes & Yes & Yes & Yes \\ Resist. & & Yes & Yes & Yes & Yes \\ Drone Capture & N/A & Yes & Partial & Partial & Yes & **Good** \\ Forward & Limited & Yes & No & Yes & Yes & **Limited** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of security and functionality features.
## Appendix D Derivation of Equations (52) and (53)  
The boundary conditions for the scattering field on the cylinder yield  
\[\sum\limits_{n}\Big{(}A_{n}J_{n}\big{(}k\sqrt{\epsilon}a\big{)}-B_{n}H_{n}^{( 1)}(ka)\Big{)}e^{in\boldsymbol{\varphi}}=\frac{i}{4}\int\limits_{S}H_{0}^{(1) }\big{(}k\sqrt{\epsilon}|\mathbf{a}-\boldsymbol{\rho}^{\prime}|\big{)}U_{k0} \big{(}\boldsymbol{\rho}^{\prime}\big{)}d\boldsymbol{\rho}^{\prime},\] (A29)  
\[k\!\!\sum\limits_{n}\Big{(}A_{n}\sqrt{\epsilon}\boldsymbol{\ell}^{\prime}{}_{n }\big{(}k\sqrt{\epsilon}a\big{)}-B_{n}H_{n}^{(1)}(ka)\Big{)}e^{in\boldsymbol{ \varphi}}=\frac{i}{4}\!\int\limits_{S}\frac{\partial}{\partial\rho}H_{0}^{(1) }\big{(}k\sqrt{\epsilon}|\boldsymbol{\rho}-\boldsymbol{\rho}^{\prime}|\big{)} |_{\boldsymbol{\rho}\to\mathbf{a}}\,U_{k0}\big{(}\boldsymbol{\rho}^{\prime} \big{)}d\boldsymbol{\rho}^{\prime},\] (A30)  
where \(\mathbf{a}\in l\), \(l\) is the contour of the cylinder, \(|\mathbf{a}-\boldsymbol{\rho}^{\prime}|=\sqrt{a^{2}+{\rho^{\prime}}^{2}+2a \rho^{\prime}\cos(\varphi-\boldsymbol{\varphi}^{\prime})}\). For the integration of the right-hand sides in Equations (A29) and (A30), we use the addition theorem for cylindrical functions [59]  
\[H_{0}^{(1)}\big{(}k\sqrt{\epsilon}|\mathbf{a}-\boldsymbol{\rho}^{\prime}| \big{)}=\begin{cases}\sum\limits_{n}H_{n}^{(1)}\big{(}k\sqrt{\epsilon}\rho^{ \prime}\big{)}J_{n}\big{(}k\sqrt{\epsilon}\rho\big{)}e^{in(\varphi-\boldsymbol {\varphi}^{\prime})},\rho\leq\rho^{\prime}\\ \sum\limits_{n}H_{n}^{(1)}\big{(}k\sqrt{\epsilon}\rho\big{)}J_{n}\big{(}k\sqrt{ \epsilon}\rho^{\prime}\big{)}e^{in(\varphi-\boldsymbol{\varphi}^{\prime})}, \rho\geq\rho^{\prime}.\end{cases}\] (A31)  
Integrating over azimuthal dimension, we obtain  
\[A_{n}J_{n}\big{(}k\sqrt{\epsilon}a\big{)}-B_{n}H_{n}^{(1)}(ka)=\frac{i}{4}f_{nk }H_{n}^{(1)}\big{(}k\sqrt{\epsilon}a\big{)},\] (A32)  
\[A_{n}\sqrt{\epsilon}\boldsymbol{\ell}^{\prime}{}_{n}\big{(}k\sqrt{\epsilon}a \big{)}-B_{n}H_{n}^{(1)}(ka)=\frac{i}{4}\sqrt{\epsilon}f_{nk}H_{n}^{(1)}\big{(} k\sqrt{\epsilon}a\big{)},\] (A33)  
where  
\[f_{nk}=\int\limits_{S}J_{n}\big{(}k\sqrt{\epsilon}\rho^{\prime}\big{)}e^{-in \boldsymbol{\varphi}^{\prime}}U_{k0}\big{(}\boldsymbol{\rho}^{\prime}\big{)}d \boldsymbol{\rho}^{\prime}.\] (A34)Equations (A32) and (A33) form a system of two linear algebraic equations with respect to the coefficients \(A_{n}\), \(B_{n}\). Finding \(A_{n},B_{n}\), substituting them to Equations (49) and (50), and using the Wronskian of cylindrical functions, we obtain Equations (52) and (53).
# A Fast and Reliable Luma Control Scheme for High-Quality HDR/WCG Video
## 3 Linear Approximation of EOTF
### Proposed Linear Approximation  
In order to exploit the derived bounds for the linear approximation of EOTE, let us first consider the straight line passing the nonlinear and linear pair of the original color signal, \((X^{\prime},X)\), and the pair of the reconstructed color signal from \(\hat{Y}^{\prime}_{min}\), \(\big{(}\hat{X}^{\prime}_{min},\hat{X}_{min}\big{)}\), where \(X\in\{R,G,B\}\). If we denote the slope of this line by \(s_{X}\), then the EOTF for each reconstructed signal, \(\hat{X}^{\prime}_{min}\leq\hat{X}^{\prime}\leq\hat{X}^{\prime}_{max}\), can be represented by  
\[L\big{(}\hat{X}^{\prime}\big{)}=L\big{(}X^{\prime}\big{)}+s_{X}\cdot\Delta X+ \delta_{\hat{X}^{\prime}} \tag{20}\]  
where \(\Delta X=\hat{X}^{\prime}-X^{\prime}\) and \(\delta_{\hat{X}^{\prime}}\) denote the error between the EOTF and the considering straight line at \(\hat{X}^{\prime}\). Note that this representation is not an approximation of the EOTF with the appropriate value of \(\delta_{\hat{X}^{\prime}}\), which is always positive for every \(\hat{X}^{\prime}\) satisfying \(\hat{X}^{\prime}_{min}\leq\hat{X}^{\prime}\leq\hat{X}^{\prime}_{max}\) because of the monotonically increasing nature of the EOTF. With this representation, the minimization of (2) yields the optimum solution of  
\[\hat{Y}^{\prime}_{O}=\frac{\sum_{X\in\{R,G,B\}}w_{X}s_{X}e_{X}}{\sum_{X\in\{R, G,B\}}w_{X}s_{X}}-\Delta,\Delta=\frac{\sum_{X\in\{R,G,B\}}w_{X}\delta_{\hat{X}^{ \prime}}}{\sum_{X\in\{R,G,B\}}w_{X}s_{X}} \tag{21}\]  
which comprises the linear approximation using the considering straight line in the first part and the following error correction term of \(\Delta\). Hence, (21) shows the optimal solution \(\hat{Y}^{\prime}_{O}\) is always smaller than the solution from the straight line passing the two points \((X^{\prime},X)\) and \(\big{(}\hat{X}^{\prime}_{min},\hat{X}_{min}\big{)}\). Likewise, with the straight line passing the two points \((X^{\prime},X)\) and \(\big{(}\hat{X}^{\prime}_{max},\hat{X}_{max}\big{)}\) for each color \(X\in\{R,G,B\}\), we can observe that true optimum is always larger than the approximate solution using the line.  
Based on these two observations, we decided to use the straight line passing the two points \((X^{\prime},X)\) and \(\big{(}\hat{X}^{\prime}_{M},\hat{X}_{M}\big{)}\) for each color \(X\in\{R,G,B\}\) as the proposed linear approximation of the EOTF, where  
\[\hat{X}_{M}=\big{(}a\cdot\hat{X}_{min}+b\cdot\hat{X}_{max}\big{)}\,/(a+b)\text{ and }\hat{X}^{\prime}_{M}=L^{-1}\big{(}\hat{X}_{M}\big{)} \tag{22}\]With this proposed linear approximation, the proposed luma value will be  
\[\hat{Y}_{P}^{\prime}=\frac{\sum_{X\in\{R,G,B\}}w_{X}s_{X}e_{X}}{\sum_{X\in\{R,G, B\}}w_{X}s_{X}},s_{X}=\frac{\bar{X}_{M}-X}{\bar{X}_{M}^{\prime}-X^{\prime}} \tag{23}\]
# Automatic Identification of Peanut-Leaf Diseases Based on Stack Ensemble
## 3 Results  
### Deep-Learning Model and Traditional Machine-Learning Model Accuracy  
The expression of the accuracy rate (\(Acc\)) of this experiment's calculation model was calculated as in Equation (1):  
\[Acc=\frac{1}{n_{n}}\sum\limits_{i=1}^{n_{n}}\frac{n_{i}}{n_{i}} \tag{1}\]  
where \(n_{n}\) is the total number of sample classes, \(i\) is the class label, \(n_{ii}\) is the number of samples predicted as class I, and \(n_{i}\) is the total number of samples in class I.  
In Table 2, we can see that among the five CNN models, the accuracy of DenseNet121 in the test set was 95.98%, which was higher than other models. The accuracies of InceptionV3, ResNet50, AlexNet, and VGG16 in the test set were 95.07%, 95.25%, 91.79%, and 93.35%, respectively. In the traditional machine-learning method, SVM achieved an accuracy of 73.20%, which is 11.77% higher than the lowest LR of 61.43%. Compared with the convolutional neural network, the traditional machine-learning methods were much less effective in identification. Convolutional neural networks can automatically extract useful features of images through convolutional layers, and then summarize and classify these features nicely through the final fully connected layer.
###### Abstract
This study aims to enhance the efficiency and sustainability of biomass gasification processes by reliably and rapidly predicting the levels of CO, CO\({}_{2}\), H\({}_{2}\), and CH\({}_{4}\) gases through machine learning algorithms, including RF, XGBoost, LightGBM, Elastic Net, AdaBoost, GBR, KNN, and DT. The dataset used for training and testing the models in the study was compiled from data collected from 45 different articles. The created dataset consists of 12 input parameters and four output parameters (CO, CO\({}_{2}\), H\({}_{2}\), and CH\({}_{4}\)), with each output being predicted separately. The input parameters in the dataset are important as they reflect the chemical and physical properties of biomass and play a critical role in determining the quantity of gases generated during the gasification process. The prediction performance of the machine learning models was optimized by determining the best model hyperparameter combinations under both optimal and suboptimal conditions using the Bayes optimization method. This optimization process enabled the identification of not only the most successful model but also the most robust model. As a result, the prediction of gas composition in the biomass gasification process has become more accurate and reliable, and the efficiency of the process and its environmental impacts can be controlled more effectively. The contributions of this study to the literature are as follows:  
* Optimum hyperparameter combinations for predicting biomass gas composition using machine learning were determined through Bayesian optimization. * Machine learning model performance was analyzed under both optimal and suboptimal conditions to identify the most accurate and robust model. * The predictive capability of the models was generalized using a comprehensive dataset aggregated from various studies. * The statistical significance of model performance under optimal and suboptimal conditions was evaluated using _t_-tests and Cohen's d.
## 3 Results and Discussion
This observation implies that the inception of the first vibrational rarefaction wave, which was initially thought to be directly triggered by the compression pressure due to bubble expansion and breakdown shock wave in confined conditions [23], is also associated with liquid-mass oscillations. Specifically, the expansion of the laser bubble initiates liquid-mass oscillations, and each pressure increase driven by these oscillations propels the outward movement of cuvette walls, resulting in a sequence of vibrational rarefaction waves, as shown in Figure 4. For large laser bubbles, the first appearance of compression pressure was significantly amplified owing to the superimposition of the pressure rises induced by liquid-mass oscillations and laser bubble expansion. This, in turn, results in a more pronounced wall vibration than the subsequent series of vibrations. Consequently, the first vibrational rarefaction wave exhibits a more robust tensile component compared to the others with increasing laser bubble size. Moreover, the initial tensile phase of each vibrational rarefaction wave may also be further reinforced directly by the tensile pressure induced by liquid-mass oscillation. Considering the intricate structures of both the walls and the liquid, it is reasonable to assume that they possess multiple eigen-oscillation modes. The shift in the eigen-oscillation mode of liquid-mass oscillation was confirmed through the variation in the time interval between adjacent secondary bubble dynamics as \(T_{\text{osc1}}\) increases [25]. However, detecting the change in the eigen-oscillation mode of the cuvette wall using current methods remains a challenge. The use of interference techniques for directly detecting wall motion offers a potential avenue for measurement and will be explored in future investigations. In general, this work involves several key mechanisms behind the generation of additional vibrational pressure waves. First, the expansion of the bubble leads to extra compression pressure due to confinement. Furthermore, bubble expansion triggers the oscillation of the liquid with finite volume, resulting in another pressure fluctuation within the liquid. Together, it can induce damped vibrations in the surrounding walls, ultimately giving rise to a sequence of vibrational rarefaction waves with strong tensile components. Such vibrational tensile waves were enhanced due to their superposition with the tensile part of the liquid-mass oscillation-induced wave, thereby significantly altering the dynamics of the laser bubble and driving the expansion of secondary bubbles. Although only cuvette wall size and liquid volume on the vibrational rarefaction waves are confirmed in this work, it is reasonable for us to deduce that the rarefaction wave generation and resulting complex bubble dynamics also can be influenced by other properties of the wall and the liquid, such as the geometry and material as well as the mechanical properties, since all of these are associated with the system's eigen-frequency.
# Dual Hybrid Attention Mechanism-Based U-Net for Building Segmentation in Remote Sensing Images
## 4 Results and Analysis
### Massachusetts Dataset
#### 4.1.3 Attention Module-Based Transfer Learning Experiment  
To assess the migration ability of the DHA module, we integrate it into the SegNet architecture and evaluate its impact on the segmentation performance. The training process is conducted in the same experimental environment as the other models, utilizing uniform training parameters and training from scratch without pre-trained models. The training results show the excellent transferability of the DHA module. The specific quantitative experimental results are shown in Table 4, and a visual comparison of the network effects is shown in Figure 7.  
Compared with the original SegNet network, SegNet + DHA presents great improvements in terms of the accuracy, mIoU, recall, and F1 score metrics, achieving values of 0.9759, 0.8884, 0.8817, and 0.8911, respectively. Figure 8 depicts the accuracy curves during the training processes of SegNet and SegNet + DHA. The smoothness of the curves indicates that the DHA module possesses good transferability, effectively enhancing the segmentation accuracy and enabling quick convergence. These results provide evidence of the substantial improvement achieved by the DHA module when integrated into SegNet, validating its effectiveness in enhancing the segmentation performance for urban remote sensing building images.  
Figure 7 provides a visualization of test results obtained when migrating the DHA module to SegNet. As shown in the red boxes in Image 10, SegNet and the SegNet + DHA have different degrees of incorrect segmentation, but SegNet + DHA has fewer errors regarding the recognition and segmentation of certain relatively hidden buildings. For some remote sensing images with more complex environments, as shown in Image 11, the addition of the DHA module to SegNet enables it to pay more attention to building information, resulting in better preservation of edge details. Moreover, SegNet + DHA significantly improves the completeness of building segmentation compared to SegNet in Image 11, demonstrating the effectiveness of the proposed DHA module for conducting building segmentation in remote sensing images.  
Figure 6: Visualization of experimental prediction results of ablation learning on attention mechanisms on the Massachusetts dataset. (**a**) Original, (**b**) U-Net + CBAM, (**c**) U-Net + SE, (**d**) U-Net + HAM-1, (**e**) U-Net + HAM-2, and (**f**) DHAU-Net.  
Figure 8: The iterative training accuracy curves of SegNet and SegNet + DHA.  
Figure 7: Visualization of the experimental prediction results. (**a**) Original, (**b**) SegNet, and (**c**) SegNet + DHA.