esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Discussions  
The proposed algorithm provides a better performance and works well even with the existence of exudative lesions or bright artifactual features. However, it tends to produce false fovea detection if there are large hemorrhages around the fovea, especially in the STARE dataset. Another factor decreasing the precision of the method is raised by the vessel segmentation due to the presence of heavy pathologies. A novel vessel segmentation algorithm [25; 26; 27] may be taken into account to improve the vessel segmentation in the future.  
The retinopathy analysis depends on the fovea localization. The detection of hard exudates is not enough to grade the diabetic macular edema, because the distribution of exudates around the fovea also needs to be considered. Therefore, the proposed approach for locating fovea may provide a better macular detection for further disease detection.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Method** & **DRIVE** & **STARE** \\ \hline Proposed method & 9.8 & 30.7 \\ [PERSON] _et al._[23] & 13.1 & 79.7 \\ [PERSON] _et al._[22] & 23.9 & 53.1 \\ [PERSON] _et al._[24] & 16.2 & - \\ [PERSON] _et al._[4] & 19.1 & 56.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The average of the Euclidean distance (in pixels) between the fovea obtained by each method and that given by experts.  
Figure 9: The comparison of Euclidean distance between the fovea location in each method and expert location (STARE).  
In this paper, we supposed that the fovea is considered as a point (as most of the published works did), and evaluated the performance by measuring the distance between the located fovea center and the ground truth point. The measurement of the fovea area is challenging work that necessitates future development.
## 3 Materials and Methods
### Formative Assessment Algorithms
#### 3.2.2 Other Formative Assessment Algorithms  
The Elo-bandit algorithm proposed in this article is compared to the previously presented upper confidence bound based algorithm, and two classical formative assessment methods, choosing questions at random and sequential enumeration through topics.  
The upper confidence bound algorithm identifies and explores the weakest topic within the quiz material, without taking in account item difficulty or the order in which those items ought to be studied to improve topic knowledge [45]. UCB algorithm uses the same principle to select a topic as EBA, Equation (1). Except, it is based on classical test theory and uses a mean answer value instead of Elo rating estimation to denote student's knowledge of the topic. In practice, UCB based algorithm tends to first explore all topics, as exploration parameter starts large. As the number of questions answered on a particular topic increases, so the uncertainty parameter decreases and the mark on the topic decides which topic shall be further explored by the algorithm.  
The random algorithm simply selects a random question out of the pool of yet unseen by the student items. The enumerate topics algorithm serves a random question from each unexhausted topic in sequence. Its operation resembles UCB when its operation is dominated by the exploration component.  
Figure 2: Item Elo rating estimation approaching MLE estimation, on the left. On the right, student’s topic knowledge estimation.
## 2 Methods
### Proposed Spatial and Angular SCF Beamformer  
Conventional SCF splits the phase range into two parts according to the sign of the signal [18]. RF signals could be approximately identified relevant if their phases all belong to a certain part. The SCF can be calculated as:  
\[SCF(k)=1-\sqrt{1-\left[\frac{1}{M}\sum_{j=1}^{M}b_{j}(\mathbf{k})\right]^{2}}, \tag{7}\]  
where \(b\) is the sign bit of the aperture data and \(M\) is the element number of the aperture.  
Conventional SCF relies on the phase coherence among the receiving backscattered signals at each element of the aperture, which is caused by the distance difference from the scatter point to each of these elements [24]. It also simplified the calculation of phase coherence by using the sign bit of RF data. As it focuses on the similarity between the channel signals received on transducer elements which differ in space, conventional SCF acts as a spatial filter. To emphasize the characteristics of its attention to spatial information, we name it spatial SCF (sSCF).  
Based on the CPWC beamforming in Section 2.1, the spatial sign bit \(b\) of the RF signal data must be in matrix form as:  
\[b_{ij}(\mathbf{k})=\left\{\begin{array}{ccc}-1&if&x_{i,j}(\mathbf{k})<0\\ 1&if&x_{i,j}(\mathbf{k})\geq 0\end{array}\right.. \tag{8}\]  
The sSCF focuses on the information at the aperture within every angle, so the standard deviation \(\sigma\) of sSCF is  
\[\sigma_{i}(k)=\sqrt{1-\left(\frac{1}{M}\sum_{j=1}^{M}b_{i,j}(\mathbf{k})\right) ^{2}}, \tag{9}\]  
where the subscript \(i\) of \(\sigma_{i}\) means every \(X_{c}^{i}\) has its own \(\sigma\). And the beamformer weights \(sSCF_{i}(\mathbf{k})\) and final output \(Y_{s}(\mathbf{k})\) for CPWC could be expressed as  
\[sSCF_{i}(k)=1-\sigma_{i}=1-\sqrt{1-\left(\frac{1}{M}\sum_{j=1}^{M}b_{i,j}( \mathbf{k})\right)^{2}}, \tag{10}\]  
\[Y_{s}(\mathbf{k})=\frac{1}{N}\sum_{i=1}^{N}sSCF_{i}(k)X_{c}^{i}(\mathbf{k}), \tag{11}\]  
where the subscript \(S\) of \(Y_{s}(\mathbf{k})\) means'spatial'. Its flow diagram is shown as Figure 2b.  
The sSCF beamformer measures the spatial coherence of the RF data. Based on the angular coherence presented in [20], the angular SCF (aSCF) beamformer can be proposed as well.  
The aSCF beamformer operates on superposition data rather than postsummed RF data. The angular sign bit of the CPWC data can be shown as:  
\[b_{i}(\mathbf{k})=\left\{\begin{array}{ccc}-1&if&X_{c}^{i}(k)<0\\ 1&if&X_{c}^{i}(k)\geq 0\end{array}\right.. \tag{12}\]  
The aSCF focuses on the information with the angles, so the standard deviation of \(b_{i}(\mathbf{k})\), aSCF and output \(Y_{a}(\mathbf{k})\) are shown as:  
\[\sigma(k)=\sqrt{1-\left(\frac{1}{N}\sum_{i=1}^{N}b_{i}(\mathbf{k})\right)^{2}}, \tag{13}\]  
\[aSCF(k)=1-\sigma=1-\sqrt{1-\left(\frac{1}{N}\sum_{i=1}^{N}b_{i}(\mathbf{k}) \right)^{2}}, \tag{14}\]\[Y_{a}(\mathrm{k})=\frac{1}{N}aSCF(\mathrm{k})\sum_{i=1}^{N}X_{c}^{i}(\mathrm{k}). \tag{15}\]  
The subscript \(a\) of \(Y_{a}(\mathrm{k})\) means 'angular' and its framework is described in Figure 2c.
## 4 Method Analysis and Control Strategy Development  
In this study, the 01-11 gear shifting scheme was studied as a typical shift condition. The control flow chart of gears 01-11 is shown in Figure 5. When the vehicle operated in the initial gear (gear 01), EM1 was in the stopping state, and EM2 transmitted power to the output shaft of the gearbox through the G1 gear pair. When the shift schedule was triggered, the synchronizer remained engaged with the G1 gear pair; the rotary velocity of the active end of CL2 was calculated; EM1 was started; the rotary velocity was regulated with the rotary velocity of the active end of CL2 as the target rotary velocity (definition: the clutch friction plate near the EM1 side was the driven end of the clutch, and the clutch friction plate near the gearbox input shaft side was the active end of the clutch). While regulating the rotary velocity of EM1, the initial engagement oil pressure of CL2 was controlled. When the rotary velocities of the active and driven ends of CL2 were basically the same and the initial engagement oil pressure of CL2 was established, CL2 began to be engaged. After CL2 was fully engaged, the output torques of EM2 and EM1 were coordinated to ensure that the total torque of the gearbox output shaft remained stable. After shifting, EM1 and EM2 drove the vehicle together. The power of the motors was transmitted to the output shaft of the gearbox through the G1 gear pair, and the vehicle ran in the new gear (gear 11). This shift process included two important problems: the motor rotary velocity control (Section 4.1) and coordinated control of the motor rotary velocity and clutch initial engagement oil pressure (Section 4.2).
## 2 Materials and Methods
### Multi-Label Classification
#### 2.6.2 Performance Metrics  
In multi-label classification, there are two methods for evaluating the performance of the classifiers: example-based metrics and label-based metrics [20]. Example-based metrics compare the actual label sets for data samples to the label sets of the prediction results. Label-based metrics calculate the binary evaluation results for each label and then average the results. The evaluated binary classification results are used to generate an evaluation score for all labels.  
For a binary classification evaluation, the precision, recall and \(F_{1}\) scores are calculated [40]. A confusion matrix provides a visual indication of how closely actual data match the predicted results. It also helps us understand how the evaluation indicators are obtained. Table 5 presents a confusion matrix for the binary classification.  
_Accuracy_ is defined as follows by dividing the number of correctly predicted data by the total number of data, given as  
\[Accuracy=\frac{1}{|D|}\sum_{i=1}^{|D|}\frac{|Y_{i}\cap Z_{i}|}{|Y_{i}\cup Z_{i}|} \tag{8}\]  
where \(Y_{i}\) is the label set of the i-th data sample, and \(Z_{i}\) is the predicted label set of the \(i\)-th data sample. _Precision_ represents how much of the actual data are true among the data whose prediction values are true and is defined as  
\[Precision=\frac{1}{|D|}\sum_{i=1}^{|D|}\frac{|Y_{i}\cap Z_{i}|}{|Z_{i}|}. \tag{9}\]  
_Recall_ represents how much of the predicted data are true among data whose actual values are true, and is defined as  
\[Recall=\frac{1}{|D|}\sum_{i=1}^{|D|}\frac{|Y_{i}\cap Z_{i}|}{|Y_{i}|}. \tag{10}\]  
The \(F_{1}\) score represents the harmonic mean of the precision and recall and is defined as  
\[F_{1}=\frac{1}{|D|}\sum_{i=1}^{|D|}\frac{2|Y_{i}\cap Z_{i}|}{|Y_{i}|+|Z_{i}|}. \tag{11}\]  
To focus on incorrectly predicted labels in multi-label classification, the _Hamming loss_ is used and is given as  
\[\textit{Hamming-Loss}=\frac{1}{|D|}\sum_{i=1}^{|D|}\frac{|Y_{i}\Delta Z_{i}|}{ |L|} \tag{12}\]  
where \(\Delta\) is a symmetric difference between two sets. In this study, we applied micro-averages to the label-based metrics [20]. The micro-averaging is formulated as follows:  
\[M_{micro}=M\Bigg{(}\sum_{\lambda=1}^{|L|}TP_{\lambda},\sum_{\lambda=1}^{|L|}FP _{\lambda},\sum_{\lambda=1}^{|L|}TN_{\lambda},\sum_{\lambda=1}^{|L|}FN_{ \lambda}\Bigg{)} \tag{13}\]  
where \(\lambda\) indicates the elements in the label set.  
It is appropriate to measure the performance of a single label classifier based on how accurate it is by measuring the \(F_{1}\) score, and it is appropriate to determine how low the error is through the Hamming loss for the performance of a multi-label classifier. Therefore, in this paper, we evaluated the performance of single label classifier and multi-label classifier as different indicators [19].
## 2 Materials and Methods
### Adsorption Kinetic Models
The band detected at approximately 1040 cm\({}^{-1}\) is associated with C-O stretching, which may correspond to either carboxyl (-COOH) or ester (-COOR) groups, potentially resulting from esterification reactions between acetic acid and the organic components of the banana peel. Additionally, weak absorption bands detected at 2314 cm\({}^{-1}\) are assigned to C-H stretching modes, suggesting the presence of aliphatic structures in the banana peel. The signals appearing at 452 cm\({}^{-1}\) and 867 cm\({}^{-1}\) can be attributed to metal-oxygen (M-O) stretching vibrations, indicating the presence of trace metal oxides, such as Fe-O, Ca-O, or Mn-O in the banana peel. The application of two kinetic models within a heterogeneous system is presented for the 100% _v/v_ concentration of used palm oil in Figure 5. The corresponding parameters are provided in Table 5. The results show that the plot in Figure 5a for the pseudo-first-order kinetic model does not exhibit linearity over the entire time range, whereas the pseudo-second-order model provides a better fit, as shown in Figure 5b. This indicates that the reaction was influenced by both the used oil adsorbate and the solid adsorbent (A-BP). The adsorbent not only functioned as a catalyst but also acted as one of the reactants due to the active sites and porosity present on its surface. Figure 4: FTIR of A-BP. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Order of Reaction** & **Parameters** & \\ \hline \multirow{3}{*}{Pseudo-first-order} & k\({}_{1}\) (min\({}^{-1}\)) & 0.0042 \\ \cline{2-3}  & R\({}^{2}\) & 0.7500 \\ \cline{2-3}  & Linear equation & y = 0.0042\(\mathrm{x}-0.2111\) \\ \hline \multirow{3}{*}{Pseudo-second-order} & k\({}_{2}\) (g mg\({}^{-1}\) min\({}^{-1}\)) & 0.0103 \\ \cline{2-3}  & R\({}^{2}\) & 0.9998 \\ \cline{2-3}  & Linear equation & y = 0.0103\(\mathrm{x}-0.0003\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The reaction rates and the corresponding constants for the used oil concentration of 100% _v/v_. To enhance the reliability of this study, additional experiments were conducted to evaluate the presence of residual toxins in the A-BP adsorbent. These tests aimed to confirm its safety for use as an adsorbent in food applications, particularly in frying oil. As shown in Table 6, the analysis revealed the presence of lead (Pb) in A-CP at a concentration of 0.34 mg/kg, while other heavy metals, including arsenic (As), cadmium (Cd), and mercury (Hg), were undetectable. The detection of Pb in the sample is attributed to contamination from the storage container used for banana peels. However, the detected value remained below the standard limit of 1 mg/kg, as established by the Food and Drug Administration, Ministry of Public Health. Lead (Pb) has also been found in food products such as seasoning powder (0.37 mg/kg), fish (0.31 mg/kg), and pork (0.64 mg/kg). Therefore, the Pb level in A-BP is not hazardous and is lower than the concentrations commonly found in daily food items.
# Performance Optimization of Grounding System for Multi-Voltage Electrical Installation
###### Abstract  
Grounding systems are critical for ensuring electrical safety, fault current dissipation, and electromagnetic compatibility in power installations across different voltage levels. This research presents a comparative study on the optimization of grounding configurations for 400 V, 10 kV, and 35 kV electrical installations, focusing on key performance parameters such as grounding resistance, step and touch voltages, and fault current dissipation efficiency. The study employs computational simulations using the finite element method (FEM) alongside empirical field measurements to evaluate the influence of soil resistivity, electrode materials, and grounding configurations, including rod electrodes, grids, deep-driven rods, and hybrid grounding systems. Results indicate that soil resistivity significantly affects grounding efficiency, with deep-driven rods providing superior performance in high-resistivity conditions, while grounding grids demonstrate enhanced fault current dissipation in substations. The integration of conductive backfill materials, such as bentonite and conductive concrete, further reduces grounding resistance and enhances system reliability. This study provides engineering insights into optimizing grounding systems based on installation voltage levels, cost considerations, and compliance with IEEE Std 80-2013 and IEC 60364-5-54. The findings contribute to the development of more resilient and cost-effective grounding strategies for electrical installations.  
g 22 July 202531 July 20251 August 20251 August 20251 August 20251  
1  
Footnote 1: Centre for Electric Energy and High Voltage, CoE for Robotics and Sensing Technologies, Faculty of Artificial Intelligence and Engineering, Multimedia University, Cyberiaya 63100, Malaysia; [EMAIL_ADDRESS]  
2
Footnote 2: Department of Computer Science, College of Computing and Information Technology, Shaqra University, Shaqra 11961, Saudi Arabia; [EMAIL_ADDRESS]  
* Correspondence: [EMAIL_ADDRESS] (M.T.S.); [EMAIL_ADDRESS] (G.R.)
## 3 Development of RF WPT-Enabled IoT Application
### Implementation and Experimental Validation
#### 3.4.3 RF-DC Converter
In addition, one can see that the designed board demonstrates the peak efficiency for the input signals with units-of-milliWatt power levels, which is well in line with what is needed for our application. Note that the reported measurement results were obtained having the resistive load of 9 kOhm connected to the circuit. The level of the load was selected based on the mean power consumption of the sensor node running our target application. A few iterations of the RF-DC converter design were executed to find the appropriate balance between the efficiency and the level of the output voltage, which needs to exceed the minimum threshold voltage for sensor node's DC-DC converter. Figure 11: Schematic of the radio frequency to direct current converter board. Figure 12: Measured S11 parameter of the radio frequency to direct current converter board for different input power levels. Figure 10: A prototype of energy harvesting printed circuit board.
# Maintenance of Discovered High Average-Utility Itemsets in Dynamic Databases
## 1 Introduction
An Apriori-like approach was first designed that considers the length (size) of each itemset. It calculates the average-utility of each itemset instead of its utility as in HUIM, which provides a flexible way of measuring the importance of itemsets for decision-making. The _auub_ (Average-Utility Upper Bound) model [20] was presented to obtain a downward closure property by maintaining the HAUUBIs (High Average-Utility Upper-Bound Itemsets), thus reducing the search space to discover the set of HAUIs. [PERSON] et al. [21] developed an efficient HAUP-tree (High Average-Utility Pattern tree) to store information related to 1-HAUUBIs in a compact tree structure, and avoid candidate generation. An efficient average average-utility (AU)-list structure [22] was then developed to efficiently discover the set of HAUIs. Several algorithms [23; 24] were designed and the design of several extensions are in progress. Most approaches for HAUI mining were developed to find the set of HAUIs in a static database. When the size of a database is increased or decreased (e.g., after inserting new transactions) [25; 26; 27], existing algorithms needs to re-mine the updated database in batch mode to update the results. [PERSON] et al. [25] presented the FUP (Fast UPdated) concept to maintain the discovered frequent itemsets using incremental updates. It categorizes itemsets into four cases and the itemsets of each case are then, respectively, maintained using a designed procedure. The FUP concept was utilized in HUIM [27]. In this paper, we utilize the FUP concept to update the results when transactions are instead based on the _auub_ model and AUL-structures. An efficient FUP-HAUIMI algorithm is presented to incrementally update the discovered information. The major contributions are the following. * A FUP-HAUIMI algorithm is presented to incrementally update the discovered knowledge without candidate generation for transaction insertion. The AUL-structure is utilized in the designed algorithm to efficiently keep information for mining patterns and incrementally updating results. * The FUP concept is also utilized and modified to provide updating procedures for the FUP-HAUIMI algorithm. Based on the FUP concept, the correctness and completeness of the algorithm to update the discovered knowledge is ensured. * Experiments show that the designed FUP-HAUIMI algorithm has better performance to maintain and update the discovered HAUIs than that of the state-of-the-art HAUI-Miner algorithm running in batch mode and the state-of-the-art incremental IHAUPM algorithm.
## 4 Results and Discussion
### Empirical Estimation  
VIV amplitude estimations of the 3D prototype prism and the 2D section model were conducted for comparison with the measured experimental results according to the empirical formula proposed in the Euro Code [27]. The estimation method focuses on slender structures with a typical cross-section (rectangular cross-section is one of them) and is established on a large amount of experimental and field test data. The formula used to estimate the VIV amplitude is:  
\[\frac{A}{D}=\frac{1}{St^{2}}\cdot\frac{1}{Sc}\cdot K\cdot K_{w}\cdot C_{lat} \tag{6}\]  
where \(St\) is the Strouhal number, the empirical values for a rectangular cross-section can be found in Euro Code, \(Sc\) is the Scruton number, \(K\) is the mode shape correction factor, \(K_{w}\) is the spanwise correlation length factor, \(C_{lat}\) is the lateral force coefficient, and the boundary condition of the present 3D prototype prism is fixed. The estimated result and the values of the relevant parameters are listed in Table 2. It can be observed that the estimated result of the 3D prototype prism fits well with the experimentally measured results.
## 5 Discussion
Figure 9: Spatial distribution of the Z score of GM-XCO\({}_{2}\) fitting residuals (dXCO\({}_{2}\)) (an important criteria for extreme detection) during July–September of 2010 and March–May of 2016 (extremely high value of anomaly XCO\({}_{2}\) grids) are shown in (**a**,**b**). Positive (red) values represent high possibility of being high XCO\({}_{2}\) and verse vice. And corresponding value of differences between observation and normal state in surface skin temperature (\(\Delta\)Temp (K)), gross primary production (\(\Delta\)GPP (gC/m\({}^{2}\))) are shown in (**c**,**d**,**g**,**h**). Positive (red) values indicate high temperature and low GPP, and versa vice. Self-calibrating Palmer Drought Severity Index (scPDSI) are shown in (**e**,**f**). Negative (red) value indicates drought, and versa vice. (**a**) Z score of GOSAT dXCO\({}_{2}\) in July–September 2010; (**b**) Z score of GOSAT dXCO\({}_{2}\) in March–May 2016; (**c**) mean increased Temp. in July–September 2010; (**d**) mean increased Temp in March–May 2016; (**e**) mean scPDSI in July–September in 2010; (**f**) mean scPDSI in March–May in 2016; (**g**) mean reduced GPP in July–September in 2010; (**h**) mean reduced GPP in March–May in 2016.
# Feature-Preserved Point Cloud Simplification Based on Natural Quadric Shape Models
## 2 The Methodology
### Simplification Entropy of Key Points  
All the elements in key points belong to the data set of point cloud, \(R\), which means that the data set of key points is a subset of \(R\) that includes the critical geometric features which should be measured and quantified. The goal of FPPS is to select suitable key points which are critical for set \(\mathcal{S}\). Therefore, in FPPS, the entropy is imported into point cloud simplification for geometric feature quantification, which is defined as simplification entropy, and used to determine the information about object features, e.g., scale-keeping feature, profile and curve feature.  
The first type of simplification entropy, which measures the point's scale-keeping capability for geometric features, is called the scale-keeping simplification entropy. The second type of simplification entropy describes the degree of importance of points on the surface boundary, which is called profile-keeping simplification entropy. The last type of entropy quantifies the surface curve's information that is hidden in the points; this is called curve-keeping simplification entropy. According to the simplification entropy of point cloud, the key points can be found, and the key points are important for preserving geometric features.  
Figure 2: Before and after the normal vector’s direction adjustment. (**a**) Point clouds and their normal vectors; (**b**) point clouds and their normal vectors after normal vector direction adjustment.