esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Hybrid Harris Hawk-Arithmetic Optimization Algorithm for Industrial Wireless Mesh Networks
## 4 Results and Discussion
### Performance Analysis on Benchmark Functions
#### 4.1.3 Quantitative Analysis
The HHAOA method is able to reach the global minimum in both multimodal and fixed-dimension functions better than other techniques. HHO reached the desired value in most functions, but HHAOA converged at the exact minimum values in many cases. It's important to note that almost all algorithms converged at the desired global minimum values in F9 and F16. The MFO competed with the proposed HHAOA in mean and best value in the fixed dimension functions. At the same time, the well-performed GWO and WOA in the unimodal function have a significant setback in this multi-objective function. They struggled to reach the best value in most of the and had an almost massive difference in the final convergence values than the desired global minima. Lastly, the same results' trend has been repeated in the hybrid functions. Notably, the HHO and SCA had poor performance and these algorithms quickly progressed in the first few iterations, but then the rate of improvement slowed down. The GWO and MFO had a closer convergence value in most functions, showing the same approach to finding the best optimum values (see Figure 7), whereas the proposed HHAOA had a stable and smooth value closer to the global minima, it suggests that the algorithm converges stably. However, in F23, the HHAOA has a lower standard deviation, indicating that the algorithm consistently finds a better solution for a stable convergence. Overall, these statistical measurements prove that the convergence behavior of the HHAOA helps to identify effective search locations for improving the exploration and exploitation phases of the algorithm. The [PERSON] ranking test, which compares algorithms based on their best mean values in numerical analysis, is displayed in Table 7. In this comparative analysis, the [PERSON] ranking test is used to evaluate the performance of various metaheuristic approaches. The algorithms were ranked according to their mean value for the corresponding functions and the resulting list clearly compares each approach's effectiveness. The top-performing algorithm is awarded a rank of 1 as it is the closest to the global minimum. On the other hand, the algorithm with the lowest mean value is assigned a rank of 5, indicating its significant deviation from the desired global minimum value. In this analysis, the proposed HHAOA has the smallest average value of 1.967, securing the first rank, which signifies the technique's effectiveness in identifying the best optimum value with fewer iterations in most benchmark functions. In addition, the HHAOA has a massive 181.342% faster and best mean value compared with the SCA, which has a final average value of 5.534 with the last rank. The HHAOA has 35.587%, 64.412%, and 79.664% of increased performance than the HHO, GWO, and respectively. Lastly, the MFO has an average of 4.2 and secures fifth rank, followed by AOA with the value of 5.067, securing the second-to-last rank.
# Numerical Study of the Flow and Blockage Ratio of Cylindrical Pier Local Scour
## 4 Numerical Results  
### Live-Bed and Wide Channel Case  
As stated in Section 2, the in-house model was validated in [PERSON] et al., 2020 [20] by reproducing the experimental results in [PERSON] et al., 2005 [4]. The flow and scour conditions are detailed in the live-bed case in Table 2. In the present study, the Swamee profile defined in Equation (14) is implemented, which differs from the Poiseuille profile utilized in [PERSON] et al., 2020 [20]. Additionally, the blockage ratio for the wide channel in the present study is \(D/W=1/16\), while the blockage ratio in [PERSON] et al., 2020 [20] is \(D/W=1/30\). However, for slip walls, it is considered that the sensitivity of the flow near the obstacle to \(D/W\) is minimal when \(D/W<0.1\) ([PERSON] et al., 1995 [32] and [PERSON], 2018 [18]). Therefore, the difference in width can be considered negligible.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Symbol** & **Clear-Water Case** & **Live-Bed Case** \\ \hline Mean velocity & \(U\) & 19.23 cm/s & 46 cm/s \\ Reynolds number & \(\mathrm{Re}_{D}\) & 1923 & 46,000 \\ Friction velocity & \(u_{\mathrm{\tau}}\) & 0.9 cm/s & 1.8 cm/s \\ Water depth & \(H\) & 16 cm & 40 cm \\ Cylinder diameter & \(D\) & 1 cm & 10 cm \\ Sediment density & \(\rho_{s}\) & 2500 kg m\({}^{-3}\) & 2500 kg m\({}^{-3}\) \\ Fluid density & \(\rho\) & 1000 kg m\({}^{-3}\) & 1000 kg m\({}^{-3}\) \\ Grain size & \(d\) & 0.27 mm & 0.27 mm \\ Sand roughness & \(k_{s}\) & \(0.67\times 10^{-3}\) m & \(0.67\times 10^{-3}\) m \\ Friction Reynolds & & 6 & 12 \\ number & \(\mathrm{Re}_{\mathrm{\tau}}\) & 0.4 & 0.4 \\ Bed porosity & \(\eta\) & 0.4 & 0.4 \\ Critical Shields & \(\theta_{c_{0}}\) & 0.04 & 0.04 \\ number & \(\mu_{d}\) & 0.62 & 0.62 \\ Dynamic friction & & 0.63 & 0.63 \\ coefficient & \(\mu_{s}\) & 0.63 & 0.63 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Flow and sediment parameters used for all simulations.  
Figure 3 compares the experimental measurements from [PERSON] et al., 2005 [4] corresponding to the rough bed case with a cylinder Reynolds number \(\text{Re}_{D}=\) 46,000 with the numerical results of [PERSON] et al., 2005 [4], and the present study. These results show that the scour depth development over time can still be reproduced after the implementation of the current inflow profile. Furthermore, the current estimation is closer to the experimental data presented originally in [PERSON] et al., 2005 [4] in terms of scour depth evolution over time.
# High-Resolution Spaceborne SAR Geolocation Accuracy Analysis and Error Correction
## 3 Results
### Characteristics of the Topospheric Delay
The relationship between the ground elevation and tropospheric delay is linear, although the degree is notably weaker in the mountainous region than for the plain (Figure 13a). A linear regression analysis shows that for every 100 m increase in elevation within the study area, the tropospheric delay decreases by approximately 2.9 cm. In most cases, the residual of the fitting is less than 5 cm (Figure 13b). Given that the resolution of the raw ERA5 data is only \(0.25^{\circ}\times 0.25^{\circ}\), comparable in scale to the extent of the study area, we only obtain a single value if tropospheric correction is directly implemented based on the ERA5 data, leading to a residual of 10 cm \(\sim\) 20 cm in the tropospheric correction (Figure 13a). Therefore, the accuracy of the tropospheric correction utilizing the raw ERA5 data is inferior to that achieved using the GACOS. Owing to the continuity of the tropospheric parameters and the strong correlation between the tropospheric delay and ground elevation, the accuracies of the different tropospheric methods are close. Considering that the GACOS has  
Figure 12: (**a**) Tropospheric delay calculated using the GACOS-based ZDM method; (**b**) GACOS-based ZDM result subtracting the ERA5-based ZDM result. Figure 13: (**a**) Relationship between the ground elevation and tropospheric delay; (**b**) kernel density estimation (KDE) curve of the tropospheric delay residual estimated using the regression equation. ensuring it performs well even under complex terrain conditions and is easy to access and use, we propose that the GACOS-based ZDM method is relatively optimal.
# Highly Sensitive SPAD-Based Receiver for Dimming Control in LiFi Networks +
## 1 Introduction
### Contributions  
Motivated by a dual-mode hybrid receiver [32]:  
* We propose a novel adaptive receiver employing a SPAD array with a VOA to improve the overall performance of indoor VLC links and satisfy a wide range of dimming requirements.
* The analytical expressions of the signal-to-noise ratio (SNR), BER, and achievable data rate of the VLC dimming systems with the proposed receiver when various modulation schemes are employed are derived. The considered modulation schemes include the single-carrier schemes OOK and VOOK, and also the multi-carrier schemes DCO- and ACO-OFDM.
* We show that in low dimming levels, the proposed receiver benefits from the SPAD's high sensitivity, but as light brightness increases, the receiver utilizes the VOA to avoid significant non-linear distortion in the SPAD array and maintains superior performance.
* The extensive numerical results demonstrate that the proposed adaptive receiver outperforms both PIN PD and traditional PIN PD and SPAD receivers in terms of BER and achievable data rate under various dimming levels and a wide range of channel conditions.
## 3 Materials and Methods
### IoT Integration Process  
When the scheduled cleaning time arrives, the system automatically generates a work order. After completing the task, the cleaning staff initiates a process by scanning their RFID identification card on the reader. The RFID reader captures identity information, room details, and a timestamp, then formats this data into a structured JSON message. Instead of using the MQTT protocol, the RFID reader transmits this data via an HTTP POST request to the IoT gateway. The gateway, acting as an intermediary, validates the received data for integrity, authentication, and consistency. If the data passes validation, it is forwarded to the hospital's Hyperledger Fabric peer node via a secure HTTP REST API call. If validation fails, the data is rejected, and no further action is taken. This API interacts with a Fabric Client SDK, which converts the received data into a transaction request for the blockchain network.  
Upon reaching the peer node, the data is transmitted to the endorser nodes, which evaluate the transaction against predefined chaincode rules to ensure compliance. If the endorsement policy requirements are met, the transaction is signed and submitted to the orderer node, where it is structured into a block using the Raft consensus mechanism. The orderer then distributes this block to all relevant peer nodes, ensuring the data is permanently recorded on the blockchain ledger in an immutable and verifiable manner.  
Once the transaction is successfully committed to the ledger, a confirmation response is sent back through the same channel--from the Hyperledger Fabric network to the IoT gateway, and then to the RFID reader--informing the cleaning staff about the transaction status in real time. Additionally, the hospital's management system is integrated with the blockchain ledger through a REST API, enabling administrators to query and verify completed cleaning tasks in real time, ensuring a secure, transparent, and tamper-proof audit trail for room cleaning processes.  
Figure 2: Proposed blockchain architecture and its transaction flow.
# Machine-Learning-Based Elderly Stroke Monitoring System Using Electroencephalography Vital Signals
## 4 Experiment and Analysis
### Experiment Based on Machine-Learning Methodology
#### 4.3.1 Predicting and Analyzing Stroke Diseases Based on Power Values  
In the first experiment, 2940 data sets were extracted and tested for the elderly and the elderly with stroke power values. To verify the performance of this model, we used the performance indicators defined in Section 4.2. Tables 5 and 6 below show the prediction accuracy and the performance indicators of F1-score, recall, and precision using all 66 attributes as well as different datasets per algorithm. The second experiment performed a \(Z\)-score (normalization) for all 66 attributes. As an example of normalization application, it is necessary to prevent the patient-specific Oz_\(\theta\) value from varying in category and size of the minimum and maximum values, since there are problems that can arise depending on the unit of measure. This normalization process translates the data so that they are within a small range of 0.0 to 1.0, so that the same weights are applied for all attributes. \[\vec{x_{i}}=\ \frac{x_{i}-\mu}{\sigma}\ \times\ \alpha \tag{2}\]  
In Equation (2), \(\sigma\) and \(\mu\) are the standard deviation and mean of attribute \(x\), respectively, and \(\alpha\) is the weighted value, which is set as 1.0 in this paper. The proposed approach with Random Forest achieved over 91% in an accuracy, F1-score, Recall and Precision criteria. It also showed an accuracy of 92.52%, a F1-score of 92.5%, a recall of 92.5%, and a precision of 92.8% on the 10-fold CV dataset. It achieved over 90% in an accuracy, F1-score, \begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{2}{c}{**True Condition**} \\ \cline{3-4}  & & Stroke Elderly & Normal Elderly \\ \hline \multirow{2}{*}{**Predicted Condition**} & Stroke Elderly & True Positive & False Positive \\  & Normal Elderly & False Negative & True Negative \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Confusion matrix of performance evaluation for prognostic symptoms and prediction of stroke. In the following experiments, 36 properties were used, including brainwave information, such as \(\delta\) and \(\theta\), which are clinically associated with prior studies as well as stroke analysis based on brainwaves. In addition, properties such as \(\alpha\), \(\beta\), and \(\gamma\) were removed and used as experimental data. As with the previous experiments, each attribute was normalized using the Z-score method and tested with the resulting value. The random forest algorithm showed more than 91% performance index, and on the 10-fold CV dataset, the accuracy was 91.97%, F1-score 91.9%, recall 91.9%, and precision 92.9%. MLP showed relatively good performance with accuracy of 90.59%, F1-score 90.6%, recall 90.6%, and precision 90.7%. Other algorithms were verified through an experiment to show performance indicators of 80%. Comparing this experiment with the second experiment, it decreased
# Insider Threat Detection Using Machine Learning Approach
## 3 Materials and Methods
### Insider Threat Models
#### 3.4.2 Classification Models  
When both normal and abnormal data are available, the system can learn from both data classes. A binary classification approach is used to address the insider threat detection problem. A total of four supervised classification models are tested: SVM, Random Forest (RF), Neural Network (NN), and AdaBoost, each of which is evaluated separately.  
We used stratified k-fold cross-validation, which folds the data while maintaining the percentage of samples for each class. Furthermore, we use the Grid-Search optimisation algorithm [56] to maximise the ROC-AUC in order to have the best set of classifier hyperparameters. Since we are using cross-validation, a pipeline was used to avoid information leakage. For every fold, we used PCA and StandardScaler. PCA is used for dimensionality reduction while preserving 95% of the variance. StandardScaler standardises features by eliminating the mean and scaling to unit variance.  
Preparation for training the SVM classifier begins with tuning the C, gamma, and kernel hyperparameters via the GridSearch technique. Gamma is the parameter of a Gaussian Kernel (which is used to handle non-linear classification), and C is the parameterthat governs the cost of misclassification of the training data. A kernel translates the data to a higher dimension, allowing the classes to be separated. The parameter C presented for a range of C = {0.001, 0.05, 0.1, 1, 1.5, 10, 100, 1000}, gamma for a range of gamma = {0.01, 0.1}, and kernel for a range of r = {0.3, 0.4, 0.5, 0.6, 0.7}, kernel = {RBF, 'poly', 'gamma'}.  
The experiment's neural network design comprises an input layer, two fully connected layers, and an output layer. The majority of the learnable parameters are usually included in the fully connected layers. Before the fully connected layer, we employed Dropout to prevent over-fitting and enhance the model generalisation. Dropout provides regularisation by removing a fraction of the previous layer's outputs, forcing the network to not over-rely on any particular input. We used batch normalisation with a ratio of 0.5 to accelerate training convergence and increase overall performance. It is accomplished by normalising each feature at the batch level during training. Figure 6 shows the model architecture.  
In AdaBoost, we used a decision tree stump as a base estimator. Using a strong classifier might lead to an overfitting problem. The number of the base estimator is a significant hyperparameter that affects the classification results. We tuned the number of base estimators with the range {100, 500, 600, 700}. The learning rate in AdaBoost reflects how much each model contributes to the weights, and we tuned it using the range of {0.1, 0.5, 1}. The number of estimators in the random forest was tuned by {10, 20, 40, 50, 100, 150, 200, 500, 600}, as well as the number of features to take into account while looking for the best split = {'auto','sqrt', 'log2'}. We utilised entropy as a criterion, which is a function that measures the quality of a split in each estimator.
## 3 Results and Discussion
### \(H_{2}\)-TPR Analysis of CuO-CeO\({}_{2}\)/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\)  
Figure 5 shows the H\({}_{2}\)-TPR spectra of CuO/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\) and CuO-CeO\({}_{2}\)/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\). As the reduction temperature of CeO\({}_{2}\) is higher than 400 \({}^{\circ}\)C [14], the reduction peaks shown in the figure are all reduction signals of the Cu component. The reduction peak temperatures of CuO/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\), CuO-1% CeO\({}_{2}\)/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\), and CuO-5% CeO\({}_{2}\)/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\) were 217, 255, 262, and 270 \({}^{\circ}\)C, respectively, which correspond to the reduction of Cu\({}^{2+}\). According to the literature [26; 27], when Cu\({}^{2+}\) is doped into the lattice of CeO\({}_{2}\), a relatively stable system can be formed, resulting in an increase in the reduction temperature. The same phenomenon was observed in this experiment: the reduction temperature of CuO in CuO-CeO\({}_{2}\)/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\) was higher than that in CuO/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\), and the reduction temperature of CuO increased with increasing CeO\({}_{2}\) content. The CuO-CeO\({}_{2}\)/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\) catalysts show a strong reduction peak at approximately 220 \({}^{\circ}\)C. This peak was attributed to CuO, which was highly dispersed and weakly interacted with CeO\({}_{2}\). In addition, it is speculated that the reduction peak appears at 160 \({}^{\circ}\)C for the CuO-5% CeO\({}_{2}\)/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\) and CuO-10% CeO\({}_{2}\)/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\) catalysts is due to the electron transfer between the Cu and Ce components, and the Cu component that receives electrons can be reduced at a lower temperature. The new reduction peak appears at 160 \({}^{\circ}\)C for the CuO-5% CeO\({}_{2}\)/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\) and CuO-10% CeO\({}_{2}\)/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\) catalysts, suggesting that the loaded catalyst had more active oxygen. Evidently, the addition of CeO\({}_{2}\) improved the oxidation activity of CuO/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\) catalyst.
# Reliable Link Level Routing Algorithm in Pipeline Monitoring Using Implicit Acknowledgements
## 4 Energy and Transmission Time Analysis
### End-to-End Transmission Time
When the TXN node sends a frame to the RXN node, the node expects to receive an eACK or an iACK. The node must wait a maximum time to receive these confirmations, before concluding that the sent frame did not reach its destination because of errors in the transmission. The channel error probability is independent of the acknowledgment method used, however when there are erroneous frames, the retransmission time of the frames varies according to the approach used. IEEE 802.15.4 defines the _MacAckWaitDuration_ parameter, with a value of 54 Ts, as the \(T_{\text{ave}ACK}\) time that the TXN node must wait to retransmit a frame when it does not receive an eACK. Figure 14: Delay reduction when using iACK compared to eACK. Figure 13: Delay according to the payload size. \[T_{weACK}=54\ T_{S} \tag{6}\]  
when iACK is used, the time that the TXN node must wait to start the retransmission process of the frame, \(T_{wiAck}\), is equal to the time it takes for the RXN node to finish retransmitting the entire frame plus the \(T_{BO}\) and \(T_{CCA}\) times. This is because the TXN node must receive the frame retransmitted by the RXN node to validate the successful transmission. \[T_{wiAck}=\ T_{BO}+T_{CCA}+\ T_{frame} \tag{7}\]  
Even though the delay to retransmit the erroneous frame is greater with iACK, we must bear in mind that with iACK the retransmission of the frame in each node when there are no errors is 34 Ts faster, as mentioned above. As such, the time that is lost with iACK when the frame is retransmitted is compensated in the following hops. The number of hops required to make up the time lost due to iACK retransmission is shown in Table 2. Having into account that a typical linear infrastructure includes hundreds or thousands of nodes, there will not probably be any extra delay caused by using iACK instead of eACK. In linear structures with \(n\) nodes, the time to transmit information from node \(i\) to the extreme of the network, without collisions or lost frames in the transmission, is (for \(P_{l}\) and \(P_{r}\) flows):  
\[de(x)=T_{fra}(x)+T_{IFS}+T_{TA}+T_{BO}+T_{CCA}+\tau \tag{2}\]0  
\[de(x)=T_{fra}(x)+T_{IFS}+T_{TA}+T_{BO}+T_{CCA}+\tau \tag{2}\]1  
Since each node knows its position or address (\(i\)) within the linear infrastructure, it will select the shortest path to reach the extreme of the network, so it will select the \(P_{l}\) or \(P_{r}\) flow that minimizes the delay. Table 3 shows the time to transmit from a node to the extreme of the network with a variable number of intermediate nodes. By using the iACK mechanism the time can be reduced by 9%.